U.S. patent number 10,735,402 [Application Number 15/668,635] was granted by the patent office on 2020-08-04 for systems and method for automated data packet selection and delivery.
This patent grant is currently assigned to PEARSON EDUCATION, INC.. The grantee listed for this patent is Pearson Education, Inc.. Invention is credited to Jacob Anderson, Daniel Ensign.
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United States Patent |
10,735,402 |
Anderson , et al. |
August 4, 2020 |
Systems and method for automated data packet selection and
delivery
Abstract
Systems and methods for automated data packet selection and
delivery are disclosed herein. The system can include a memory
containing a data packet database including data packets for
delivery to a user; and a user profile database including
information identifying at least one user. The system can include a
user device and one or several servers. The one or several servers
can: receive a request for delivery of a set of data packets to a
user via the user device; identify potential data packets for
delivery to the user via the user device; determine a probability
of the user providing a desired response to each of the potential
data packets; weight the data packets according to weighting data
and the determined probability; select a set of data packets from
the potential data packets; and provide the set of data packets to
the user via the user device.
Inventors: |
Anderson; Jacob (Centennial,
CO), Ensign; Daniel (Aurora, CO) |
Applicant: |
Name |
City |
State |
Country |
Type |
Pearson Education, Inc. |
New York |
NY |
US |
|
|
Assignee: |
PEARSON EDUCATION, INC. (New
York, NY)
|
Family
ID: |
1000002818830 |
Appl.
No.: |
15/668,635 |
Filed: |
August 3, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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15360657 |
Nov 23, 2016 |
10116563 |
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15236103 |
Aug 12, 2016 |
10333857 |
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14927115 |
Oct 29, 2015 |
10218630 |
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14927145 |
Oct 29, 2015 |
10027740 |
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14927170 |
Oct 29, 2015 |
10318499 |
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14928152 |
May 30, 2017 |
9667321 |
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62320213 |
Apr 8, 2016 |
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62211156 |
Aug 28, 2015 |
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62073814 |
Oct 31, 2014 |
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62073751 |
Oct 31, 2014 |
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62072910 |
Oct 30, 2014 |
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62072914 |
Oct 30, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L
63/045 (20130101); H04L 63/0823 (20130101); H04L
67/42 (20130101); H04L 67/10 (20130101); H04L
67/2814 (20130101); H04L 67/325 (20130101) |
Current International
Class: |
H04L
29/06 (20060101); H04L 29/08 (20060101) |
Field of
Search: |
;370/477 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
International Search Report and Written Opinion dated Feb. 23,
2016, for International Application No. PCT/US2015/058467, all
pages. cited by applicant.
|
Primary Examiner: Crompton; Christopher R
Attorney, Agent or Firm: Quarles and Brady LLP
Parent Case Text
CROSS-REFERENCES TO RELATED APPLICATIONS
This application is a Continuation-in-Part of U.S. application Ser.
No. 15/360,657, filed on Nov. 23, 2016, and entitled "SYSTEM AND
METHOD FOR AUTOMATICALLY UPDATING DATA PACKET METADATA", which is a
Continuation-in-Part of U.S. application Ser. No. 15/236,103, filed
on Aug. 12, 2016, and entitled "SYSTEMS AND METHODS FOR DATA PACKET
METADATA STABILIZATION", which claims the benefit of U.S.
Provisional Application No. 62/320,213, filed on Apr. 8, 2016, and
entitled "ADAPTIVE PATHWAYS AND COGNITIVE TUTORING"; U.S.
application Ser. No. 15/236,103 claims the benefit of U.S.
Provisional Application No. 62/211,156, filed on Aug. 28, 2015, and
entitled "DATA-ENABLED SUCCESS AND PROGRESSION SYSTEM"; U.S.
application Ser. No. 15/236,103 is a Continuation-in-Part of U.S.
application Ser. No. 14/927,115, filed on Oct. 29, 2015, and
entitled "SYSTEM AND METHOD FOR INCREASING DATA TRANSMISSION RATES
THROUGH A CONTENT DISTRIBUTION NETWORK", which claims the benefit
of U.S. Provisional Application No. 62/072,910, filed on Oct. 30,
2014; U.S. application Ser. No. 15/236,103 is a
Continuation-in-Part of U.S. application Ser. No. 14/927,145, filed
on Oct. 29, 2015, and entitled "SYSTEM AND METHOD FOR INCREASING
DATA TRANSMISSION RATES THROUGH A CONTENT DISTRIBUTION NETWORK WITH
CUSTOMIZED AGGREGATIONS", which claims the benefit of U.S.
Provisional Application No. 62/073,751, filed on Oct. 31, 2014;
U.S. application Ser. No. 15/236,103 is a Continuation-in-Part of
U.S. application Ser. No. 14/927,170, filed on Oct. 29, 2015, and
entitled "CONTENT DATABASE GENERATION", which claims the benefit of
U.S. Provisional Application No. 62/072,914, filed on Oct. 30,
2014; U.S. application Ser. No. 15/236,103 is a
Continuation-in-Part of U.S. application Ser. No. 14/928,152, filed
on Oct. 30, 2015, and entitled "PREDICTIVE RECOMMENDATION ENGINE",
which claims the benefit of U.S. Provisional Application No.
62/073,814, filed on Oct. 31, 2014, the entirety of each of which
is hereby incorporated by reference herein.
Claims
What is claimed is:
1. A system for automated data packet selection and delivery, the
system comprising: memory comprising: a data packet database
comprising a plurality of data packets for delivery to a user, each
of the plurality of data packets comprising a question for
determining a skill level achieved by the user after consuming a
content in a plurality of contents, and a metadata associated with
each of the plurality of data packets, the metadata including a
difficulty level of the question; a user profile database
comprising information identifying at least one user and a user
metadata associated with each of the at least one user, wherein the
user metadata identifies an attribute of one of the at least one
user with which the metadata is associated, the attribute
comprising the skill level of the one of the at least one user; and
a user device configured to request a review of the content; one of
several servers, wherein the one or several servers are configured
to: receive, responsive to a determination that the user has
progressed through the plurality of contents beyond a review
threshold stored in the memory, wherein the review threshold
determines whether the user is authorized to access the review, a
request for delivery of a set of data packets in the plurality of
data packets to the user via the user device; identify a plurality
of potential data packets, from the plurality of data packets, for
delivery to the user via the user device; determine, according to a
statistical model using the skill level of the user stored in the
user metadata and the difficulty level of the question stored in
the data packet metadata, a probability of the user providing a
desired response to each of the plurality of potential data
packets, the desired response comprising a correct response to the
question; weight each of the plurality of potential data packets
according to a weighting data and the determined probability, the
weighting data being determined according to a weight function
comprising a weighting curve having a normal distribution
positioned with a mean determined by a second plurality of data
packets having a lowest probability of receiving from the user the
correct response to the question; select a selected set of data
packets from the plurality of potential data packets; and provide
the selected set of data packets to the user via the user
device.
2. The system of claim 1, wherein the weighting data comprises a
weighting curve, and wherein the weighting curve comprises a
truncated Gaussian distribution positioned to prefer data packets
associated with the lowest probability of the user providing the
desired response.
3. The system of claim 1, wherein the weighting data comprises a
weighting curve, and wherein the weighting curve comprises a
truncated Gaussian distribution positioned with the mode associated
with data packets associated with the lowest probability of the
user providing the desired response.
4. The system of claim 3, wherein selecting the set of data packets
from the potential data packets comprises selecting at least one
data packet for inclusion in the set of data packets.
5. The system of claim 4, wherein each selected data packet from
the at least one data packet selected for inclusion is removed from
the potential data packets.
6. The system of claim 5, wherein the one or several servers are
further configured to retrieve progress status data and a review
threshold; compare the progress status data and the review
threshold; and identify potential data packet for delivery to the
user when the comparison of the progress status data and the review
threshold indicates providing of data packets to the user.
7. The system of claim 6, wherein the one or several servers are
further configured to: categorize the potential data packets
according to the determined probability of the user providing a
desired response to each of the potential data packets; and apply
the weighting data to each of the categories of potential data
packets.
8. The system of claim 7, wherein the one or several servers are
further configured to: receive response data from the user device
to the data packets in the selected set of data packets; evaluate
the received response data; and update the user metadata based on
the received response data.
9. The system of claim 8, wherein selecting the selected set of
data packets from the potential data packets comprises: (a)
selecting a data packet for inclusion in the set of data packets;
(b) removing the selected data packet from the potential data
packets; (c) determining if the set of data packets is filled; and
(d) repeating steps (a)-(c) until the set of data packets is
filled.
10. The system of claim 9, wherein the weighting of the data
packets remains the same after the removal of the selected data
packet form the potential data packets.
11. A method for automated data packet selection and delivery, the
method comprising: receiving at one or several servers, responsive
to a determination that a user has progressed through a plurality
of contents beyond a review threshold stored in memory, the review
threshold determining whether the user is authorized to access a
review of a content, a request for delivery of a set of data
packets to the user via a user device from the user device;
identifying with the one or several servers a plurality of
potential data packets for delivery to the user via the user device
from a data packet database comprising a plurality of data packets
for delivery to the user and metadata associated with each of the
plurality of data packets, each of the plurality of data packets
comprising a question for determining a skill level achieved by the
user after consuming the content in the plurality of contents, the
data packet database coupled to a database including a user profile
database comprising data identifying at least one user and
including a user metadata identifying an attribute for the at least
one user, the attribute comprising the skill level of the at least
one user; determining with the one or several servers, according to
a statistical model using the skill level of the user stored in the
user metadata and a difficulty level of the question stored in a
data packet metadata, a probability of the user providing a desired
response to each of the plurality of potential data packets, the
desired response comprising a correct response to the question;
weighting with the one or several servers each of the plurality of
potential data packets according to a weighting data and the
determined probability, the weighting data being determined
according to a weight function comprising a weighting curve having
a normal distribution positioned with a mean determined by a second
plurality of data packets having a lowest probability of receiving
from the user the correct response to the question; selecting with
the one or several servers a selected set of data packets from the
plurality of potential data packets; and providing with the one or
several servers the selected set of data packets to the user.
12. The method of claim 11, wherein providing the set of data
packets to the user comprises generating and sending an electrical
communication to the user device, wherein the electrical
communication comprises at least one of the set of data packets as
a payload.
13. The method of claim 12, wherein weighting with the one or
several servers the data packets according to weighting data and
the determined probability further comprises retrieving weighting
data.
14. The method of claim 13, wherein the weighting data comprises a
weighting curve, wherein the weighting curve comprises a truncated
Gaussian distribution positioned with the mode associated with data
packets associated with the lowest probability of the user
providing the desired response.
15. The method of claim 14, wherein selecting the selected set of
data packets from the potential data packets comprises selecting at
least one data packet for inclusion in the set of data packets, and
wherein each selected data packet from the at least one data packet
selected for inclusion is removed from the potential data
packets.
16. The method of claim 15, further comprising: retrieving progress
status data and a review threshold; comparing the progress status
data and the review threshold; and identifying potential data
packet for delivery to the user when the comparison of the progress
status data and the review threshold indicates providing of data
packets to the user.
17. The method of claim 16, further comprising: categorizing the
potential data packets according to the determined probability of
the user providing a desired response to each of the potential data
packets; and applying the weighting data to each of the categories
of potential data packets.
18. The method of claim 17, further comprising: receiving response
data from the user device to the data packets in the provided set
of data packets; evaluating the received response data; and
updating the user metadata based on the received response data.
19. The method of claim 18, wherein selecting the selected set of
data packets from the potential data packets comprises: (a)
selecting a data packet for inclusion in the set of data packets;
(b) removing the selected data packet from the potential data
packets; (c) determining if the set of data packets is filled; and
(d) repeating steps (a)-(e) until the set of data packets is
filled, and wherein the weighting of the data packets remains the
same after the removal of the selected data packet form the
potential data packets.
Description
BACKGROUND
A computer network or data network is a telecommunications network,
which allows computers to exchange data. In computer networks,
networked computing devices exchange data with each other along
network links (data connections). The connections between nodes are
established using either cable media or wireless media. The
best-known computer network is the Internet.
Network computer devices that originate, route and terminate the
data are called network nodes. Nodes can include hosts such as
personal computers, phones, servers as well as networking hardware.
Two such devices can be said to be networked together when one
device is able to exchange information with the other device,
whether or not they have a direct connection to each other.
Computer networks differ in the transmission media used to carry
their signals, the communications protocols to organize network
traffic, the network's size, topology and organizational intent. In
most cases, communications protocols are layered on (i.e. work
using) other more specific or more general communications
protocols, except for the physical layer that directly deals with
the transmission media.
BRIEF SUMMARY
One aspect of the present disclosure relates to a system for
accelerated stabilization of data packet metadata. The system
includes a memory. The memory can include: a content database,
including a plurality of data packets and metadata identifying an
attribute of an associated data packet; and a user profile database
including user history data identifying an attribute of an
associated user. In some embodiments, each of the plurality of data
packets is associated with unique metadata, and in some
embodiments, each user is associated with unique user history data.
The system can include a user device. The user device can include:
a first network interface that can exchange data via a
communication network; and a first I/O subsystem that can convert
electrical signals to user interpretable outputs via a user
interface. The system can include one or more servers. The one or
more servers can: identify a data packet that includes content for
delivery to a user device; retrieve data packet metadata from the
content database of the memory, which data packet metadata
identifies a difficulty level of the data packet; determine that
the data packet metadata is unstable; identify a set of potential
recipients of the data packet; select one of the set of potential
recipients as the recipient of the data packet based on a
correspondence between the data packet metadata and user data from
the recipient; provide the data packet to the recipient of the data
packet; receive a response from the recipient to the provided data
packet; and automatically update the data packet metadata based on
the response received from the recipient.
In some embodiments, determining that the data packet metadata is
unstable includes retrieving a stability threshold, which stability
threshold identifies a minimum number of received user responses
subsequent to the providing of the data packet. In some
embodiments, determining that the data packet metadata is unstable
includes extracting a value indicative of the number of received
responses subsequent to the providing of the data packet from the
data packet metadata; and comparing the stability threshold and the
value indicative of the number of received responses subsequent to
the providing of the data packet from the metadata.
In some embodiments, the one or more servers include a response
processor that can translate the received response into an
observable. In some embodiments, translating the received response
into an observable includes evaluating the received response to
determine if the received response is a desired response. In some
embodiments, updating the data packet metadata includes updating a
model of the difficulty of the data packet. In some embodiments,
the model of the difficulty of the data packet is a piecewise
Gaussian distribution model.
In some embodiments, the one or more servers can repeatedly:
identify the set of potential recipients of the data packet; select
one of the set of potential recipients as the recipient of the data
packet based on a correspondence between the data packet metadata
and user data for the recipient; provide the data packet to the
recipient of the data packet; receive a response from the recipient
to the provided data packet; and automatically update the data
packet metadata based on the response received from the recipient,
until the one or more servers determine that the data packet
metadata is stable. In some embodiments, the one or more servers
can add an indicator of stability to the data packet metadata when
the one or more servers determine that the data packet metadata is
stable.
In some embodiments, the one or more servers can: automatically
generate an alert including an indicator of the stability of the
data packet metadata when the one or more servers determine that
the data packet metadata is stable; and send the alert to a
supervisor device. In some embodiments, the alert includes computer
code to direct the supervisor device to automatically launch an
application that displays an indicator of the metadata
stability.
One aspect of the present disclosure relates to a method for
accelerated stabilization of data packet metadata. The method
includes: (a) identifying a data packet that includes content for
delivery to a user device; (b) retrieving data packet metadata from
a memory, including a content library database; (c) determining
that the data packet metadata is unstable; (d) identifying a set of
potential recipients of the data packet; (e) selecting one of the
set of potential recipients as the recipient of the data packet
based on a correspondence between the data packet metadata and user
data for the recipient; (f) providing the data packet to the
recipient of the data packet; (g) receiving a response from the
recipient to the provided data packet; and (h) automatically
updating the data packet metadata based on the response received
from the recipient. In some embodiments, the data packet metadata
identifies a difficulty level of the data packet.
In some embodiments, the method includes, repeating steps (a)-(h)
until the data packet metadata is stable. In some embodiments,
selecting one of the set of potential recipients as the recipient
of the data packet includes: (i) selecting one potential recipient
of the set of potential recipients for analysis; (ii) determining a
load level for the selected one potential recipient of the set of
potential recipients; and (iii) adding a value indicative of
acceptability when a predicted load level is less than a load
threshold.
In some embodiments, selecting one potential recipient of the set
of potential recipients as the recipient of the data packet
includes generating a predicted load value that is based off of the
determined load level for the selected one potential recipient of
the set of potential recipients update to include the load effect
of receipt of the data packet. In some embodiments, the method
includes: retrieving the load threshold; comparing the predicted
load value to the load threshold; and removing the selected one
potential recipient of the set of potential recipients from the set
of potential recipient when the predicted load level is greater
than the load threshold.
In some embodiments, the method includes repeating steps (i)-(iii)
when it is determined that there are additional potential
recipients in the set of potential recipients that have not been
selected in step (i). In some embodiments, the method includes
designating a recipient associated with the value indicative of
acceptability when it is determined that there are no additional
potential recipients in the set of potential recipients that have
not been selected in step (i). In some embodiments, determining
that the data packet metadata is unstable includes retrieving a
stability threshold that identifies a minimum number of received
user responses subsequent to the providing of the data packet. In
some embodiments, determining that the data packet metadata is
unstable includes extracting a value indicative of the number of
received responses subsequent to the providing of the data packet
from the data packet metadata; and comparing the stability
threshold and the value indicative of the number of received
responses subsequent to the providing of the data packet from the
metadata.
One aspect of the present disclosure relates to a system for
content provisioning via the automatic determination of a content
attribute. The system includes a memory, including a content
database containing a plurality of data packets and metadata
identifying an attribute of an associated data packet and a user
profile database including user history data identifying an
attribute of an associated user. In some embodiments, each of the
plurality of data packets is associated with unique metadata. In
some embodiments, each user is associated with unique user history
data. The system can include a user device that can include: a
first network interface that can exchange data via a communication
network; and a first I/O subsystem that can convert electrical
signals to user interpretable outputs via a user interface. The
system can include one or more servers. The one or more severs can:
receive a response from a user via a user device, which response is
to a previously provided data packet; identify the previously
provided data packet; retrieve data packet metadata, which packet
metadata includes at least one attribute value identifying an
attribute of the data packet; translate the received response into
an observable; update the data packet metadata based on the
observable; and provide the received data packet to a user based on
the updated data packet metadata.
In some embodiments, the data packet metadata includes a model of
the difficulty level of the data packet. In some embodiments, the
model is based on a localized distribution. In some embodiments,
the localized distribution includes a piecewise Gaussian
distribution. In some embodiments, the mode of the Gaussian
distribution corresponds to a difficulty level of the data packet.
In some embodiments, the Gaussian distribution is a piecewise
Gaussian distribution.
In some embodiments, the Gaussian distribution is defined in part
by an error value corresponding to the width of the Gaussian
distribution. In some embodiments, updating the update the data
packet metadata based on the observable includes updating the
Gaussian distribution. In some embodiments, updating the Gaussian
distribution includes updating the mode of the Gaussian
distribution and the error value of the Gaussian distribution.
In some embodiments, the update of the Gaussian distribution varies
based on whether the received response is a desired response or an
undesired response. In some embodiments, providing the received
data packet to the user device based on the updated data packet
metadata of the received data packet includes selecting a recipient
of the received data packet. In some embodiments, the recipient of
the received data packet is selected based on a comparison of the
data packet metadata and user metadata associated with the
recipient.
One aspect of the present disclosure relates to a method for
automatically updating data packet metadata. The method includes:
receiving a response from a user via a user device, which response
is to a previously provided data packet; identifying the previously
provided data packet; retrieving data packet metadata, which data
packet metadata includes at least one attribute value identifying
an attribute of the data packet; translating the received response
into an observable; updating the data packet metadata based on the
observable; and providing the received data packet to a user based
on the updated data packet metadata.
In some embodiments, the data packet metadata includes a model of
the difficulty level of the data packet. In some embodiments, the
model is based on a localized distribution. In some embodiments,
the mode of the localized distribution corresponds to a difficulty
level of the data packet.
In some embodiments, the localized distribution is a Gaussian
distribution. In some embodiments, the Gaussian distribution is
defined in part by an error value corresponding to the width of the
Gaussian distribution. In some embodiments, updating the data
packet metadata based on the observable includes updating the
Gaussian distribution. In some embodiments, updating the Gaussian
distribution includes updating the mode of the Gaussian
distribution and the error value of the Gaussian distribution. In
some embodiments, the update of the Gaussian distribution varies
based on whether the received response is a desired response or an
undesired response.
In some embodiments, providing the received data packet to the user
device based on the updated data packet metadata of the received
data packet includes selecting a recipient of the received data
packet. In some embodiments, the recipient of the received data
packet is selected based on a comparison of the data packet
metadata and user metadata associated with the recipient.
One aspect of the present disclosure relates to a system for
content provisioning via the automatic determination of a content
attribute. The system includes: memory including a content
database, including a plurality of data packets and metadata
identifying an attribute of an associated data packet. In some
embodiments, each of the plurality of data packets is associated
with unique metadata. The memory includes a user profile database,
including user history data identifying an attribute of an
associated user. In some embodiments, each user is associated with
unique user history data. The system includes a user device
including: a first network interface that can exchange data via a
communication network; and a first I/O subsystem that can convert
electrical signals to user interpretable outputs via a user
interface. The system includes one or more servers. The one or more
server can, in some embodiments, be controlled according to stored
computer code. The server can: receive a data packet including
content for providing to a user via a user device; retrieve data
packet metadata associated with the data packet, which data packet
metadata includes at least one attribute value identifying an
attribute of the data packet; identify a data packet content
network relevant to the received data packet, which data packet
content network and the data packet relate to a common subject;
determine a position within the data packet network for placement
of the data packet within the data packet content network; generate
a combined attribute value based on attribute values of similarly
placed data packets in the content network; and update the data
packet metadata of the received data packet, which data packet
metadata is updated to include an attribute value matching the
generating combined attribute value of the similarly placed data
packets in the content network.
In some embodiments, the one or more servers can further determine
when the difficulty of the received data packet is identified in
the retrieved data packet metadata. In some embodiments, the one or
more servers can further identify a plurality of similarly
positioned data packets in the identified data packet content
network. In some embodiments, the plurality of similarly positioned
data packets in the identified data packet content network is
identified based on the determined position for placement of the
data packet within the data packet network.
In some embodiments, the one or more servers can further retrieve
attribute information for the identified plurality of similarly
positioned data packets. In some embodiments, the attribute
information identifies a difficulty level for each of the plurality
of similarly positioned data packets. In some embodiments, the
combined attribute value is generated based on difficulty levels
for the plurality of similarly positioned data packets.
In some embodiments, the one or more servers can further provide
the received data packet to the user device based on the updated
data packet metadata of the received data packet. In some
embodiments, providing the received data packet to the user device
based on the updated data packet metadata of the received data
packet includes selecting a recipient of the received data packet.
In some embodiments, the recipient of the received data packet is
selected based on a comparison of the data packet metadata and user
metadata associated with the recipient.
One aspect of the present disclosure relates to a method for
content provisioning via the automatic determination of a content
attribute. The method includes: receiving a data packet including
content for providing to a user via a user device; retrieving data
packet metadata associated with the data packet, which data packet
metadata includes at least one attribute value identifying an
attribute of the data packet; identifying a data packet content
network relevant to the received data packet, which data packet
content network and the data packet relate to a common subject;
determining a position within the data packet network for placement
of the data packet within the data packet content network;
generating a combined attribute value based on attribute values of
similarly placed data packets in the content network; and updating
the data packet metadata of the received data packet. In some
embodiments, the data packet metadata is updated to include an
attribute value matching the generating combined attribute value of
the similarly placed data packets in the content network.
In some embodiments, the method includes determining that the
difficulty of the received data packet is not identified in the
retrieved data packet metadata. In some embodiments, the method
includes identifying a plurality of similarly positioned data
packets in the identified data packet content network. In some
embodiments, the plurality of similarly positioned data packets in
the identified data packet content network are identified based on
the determined position for placement of the data packet within the
data packet network.
In some embodiments, the method includes retrieving attribute
information for the identified plurality of similarly positioned
data packets. In some embodiments, the attribute information
identifies a difficulty level for each of the plurality of
similarly positioned data packets. In some embodiments, the
combined attribute value is generated based on difficulty levels
for the plurality of similarly positioned data packets.
In some embodiments, the method includes providing the received
data packet to the user device based on the updated data packet
metadata of the received data packet. In some embodiments,
providing the received data packet to the user device based on the
updated data packet metadata of the received data packet includes
selecting a recipient of the received data packet. In some
embodiments, the recipient of the received data packet is selected
based on a comparison of the data packet metadata and user metadata
associated with the recipient.
One aspect of the present disclosure relates to a system for
automated data packet selection and delivery. The system includes:
memory including: a data packet database including data packets for
delivery to a user and metadata associated with the data packets;
and a user profile database, including information identifying a
plurality of users and user metadata associated with each of the
plurality of users, which user metadata identifies and attribute of
the one of the plurality of users with which the metadata is
associated. The system includes: a user device; and one of several
servers. In some embodiments, the one or several servers can:
receive a request for delivery of a set of data packets to a user
via the user device; identify potential data packets for delivery
to the user via the user device; determine a probability of the
user providing a desired response to each of the potential data
packets; weight the data packets according to weighting data and
the determined probability; select a set of data packets from the
potential data packets; and provide the set of data packets to the
user via the user device.
In some embodiments, the weighting data can include a weighting
curve, and the weighting curve can include a truncated normal
distribution positioned to prefer data packets associated with the
lowest probability of the user providing the desired response. In
some embodiments, the weighting data includes a weighting curve,
and the weighting curve includes a truncated normal curve
positioned with the mean associated with data packets associated
with the lowest probability of the user providing the desired
response. In some embodiments, selecting a set of data packets from
the potential data packets includes selecting a plurality of data
packets for inclusion in the set of data packets. In some
embodiments, each selected data packet from the plurality of data
packets selected for inclusion is removed from the potential data
packets.
In some embodiments, the one or several servers can retrieve
progress status data and a review threshold; compare the progress
status data and the review threshold; and identify potential data
packet for delivery to the user when the comparison of the progress
status data and the review threshold indicates for providing of
data packets to the user. In some embodiments, the one or several
servers can: categorize the potential data packets according to the
determined probability of the user providing a desired response to
each of the potential data packets; and apply the weighting data to
each of the categories of potential data packets.
In some embodiments, the one or several servers can: receive
response data; and update user metadata based on the received
response data. In some embodiments, selecting the set of data
packets from the potential data packets includes: (a) selecting a
data packet based for inclusion in the set of data packets; (b)
removing the selected data packet from the potential data packets;
(c) determining if the set of data packets is filled; and (d)
repeating steps (a)-(c) until the set of data packets is filled. In
some embodiments, the weighting of the data packets remains the
same after the removal of the selected data packet form the
potential data packets.
One aspect of the present disclosure relates to a method for
automated data packet selection and delivery. The method includes:
receiving at one or several servers a request for delivery of a set
of data packets to a user via a user device from the user device;
identifying with the one or several servers potential data packets
for delivery to the user via the user device from a data packet
database including data packets for delivery to the user and
metadata associated with the data packets; determining with the one
or several servers a probability of the user providing a desired
response to each of the potential data packets; weighting with the
one or several servers the data packets according to weighting data
and the determined probability; selecting with the one or several
servers a set of data packets from the potential data packets; and
providing with the one or several servers the set of data packets
to the user.
In some embodiments, providing the set of data packets to the user
includes generating and sending an electrical communication to the
user device, which electrical communication includes at least one
of the set of data packets as a payload. In some embodiments,
weighting with the one or several servers the data packets
according to weighting data and the determined probability further
includes retrieving weighting data. In some embodiments, the
weighting data can include a weighting curve, which weighting curve
can include a truncated normal curve positioned with the mean
associated with data packets associated with the lowest probability
of the user providing the desired response.
In some embodiments, selecting a set of data packets from the
potential data packets includes selecting a plurality of data
packets for inclusion in the set of data packets, and each selected
data packet from the plurality of data packets selected for
inclusion can be removed from the potential data packets. In some
embodiments, the method includes: retrieving progress status data
and a review threshold; comparing the progress status data and the
review threshold; and identifying potential data packet for
delivery to the user when the comparison of the progress status
data and the review threshold indicates for providing of data
packets to the user.
In some embodiments, the method includes: categorizing the
potential data packets according to the determined probability of
the user providing a desired response to each of the potential data
packets; and applying the weighting data to each of the categories
of potential data packets. In some embodiments, the method further
includes: receiving response data from the user device to the data
packets in the provided set of data packets; evaluating the
received response data; and updating user metadata based on the
received response data. In some embodiments, selecting the set of
data packets from the potential data packets includes: (a)
selecting a data packet for inclusion in the set of data packets;
(b) removing the selected data packet from the potential data
packets; (c) determining if the set of data packets is filled; and
(d) repeating steps (a)-(c) until the set of data packets is
filled. In some embodiments, the weighting of the data packets
remains the same after the removal of the selected data packet from
the potential data packets.
One aspect of the present disclosure relates to a method for
modifying data packet metadata. The method includes: identifying an
unstable data packet, which data packet includes content for
delivery to a user device; retrieving data packet metadata, which
data packet metadata identifies a difficulty level of the data
packet; selecting a recipient of the data packet based on the data
packet metadata and user data for the recipient; receiving a
response from the recipient subsequent to providing the data packet
to the recipient; and automatically updating the data packet
metadata based on the response.
Further areas of applicability of the present disclosure will
become apparent from the detailed description provided hereinafter.
It should be understood that the detailed description and specific
examples, while indicating various embodiments, are intended for
purposes of illustration only and are not intended to necessarily
limit the scope of the disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram illustrating an example of a content
distribution network.
FIG. 2 is a block diagram illustrating a computer server and
computing environment within a content distribution network.
FIG. 3 is a block diagram illustrating an embodiment of one or more
data store servers within a content distribution network.
FIG. 4 is a block diagram illustrating an embodiment of one or more
content management servers within a content distribution
network.
FIG. 5 is a block diagram illustrating the physical and logical
components of a special-purpose computer device within a content
distribution network.
FIG. 6 is a block diagram illustrating one embodiment of the
communication network.
FIG. 7 is a block diagram illustrating one embodiment of user
device and supervisor device communication.
FIG. 8 is a schematic illustration of one embodiment of a computing
stack.
FIG. 9A is a schematic illustration of one embodiment of
communication and processing flow of modules within the content
distribution network.
FIG. 9B is a schematic illustration of another embodiment of
communication and processing flow of modules within the content
distribution network.
FIG. 9C is a schematic illustration of an additional embodiment of
communication and processing flow of modules within the content
distribution network.
FIG. 10A is a flowchart illustrating one embodiment of a process
for data management.
FIG. 10B is a flowchart illustrating one embodiment of a process
for evaluating a response.
FIG. 11 is a flowchart illustrating one embodiment of a process for
automatic difficulty determination of the data packet and placement
of that data packet in the content network.
FIG. 12 is a flowchart illustrating one embodiment of a process for
data packet metadata stabilization,
FIG. 13 is a flowchart illustrating one embodiment of a process for
selecting recipient of the data packet.
FIG. 14 is a flowchart illustrating one embodiment of a process for
automatically updating data packet metadata and/or automatically
updating a data packet model.
FIG. 15 is an illustration of one embodiment of a piece-wise
Gaussian distribution.
FIG. 16 is an illustration of a plurality of piecewise Gaussian
distributions.
FIG. 17 is a flowchart illustrating a first portion of one
embodiment of a process for data packet selection and delivery.
FIG. 18 is a flowchart illustrating a second portion of one
embodiment for data packet selection and delivery.
FIG. 19 is an illustration of one embodiment of a weighting
curve.
In the appended figures, similar components and/or features may
have the same reference label. Further, various components of the
same type may be distinguished by following the reference label by
a dash and a second label that distinguishes among the similar
components. If only the first reference label is used in the
specification, the description is applicable to any one of the
similar components having the same first reference label
irrespective of the second reference label.
DETAILED DESCRIPTION
The ensuing description provides illustrative embodiment(s) only
and is not intended to limit the scope, applicability or
configuration of the disclosure. Rather, the ensuing description of
the illustrative embodiment(s) will provide those skilled in the
art with an enabling description for implementing a preferred
exemplary embodiment. It is understood that various changes can be
made in the function and arrangement of elements without departing
from the spirit and scope as set forth in the appended claims.
This application is related to U.S. application Ser. No.
15/236,196, filed on Aug. 12, 2016, and entitled "SYSTEMS AND
METHODS OF EVENT-BASED CONTENT PROVISIONING", now U.S. Pat. No.
10,043,133; U.S. application Ser. No. 15/236,275, filed on Aug. 12,
2016, and entitled "SYSTEM AND METHOD FOR AUTOMATIC CONTENT
AGGREGATION GENERATION", now U.S. Pat. No. 10,325,215; and U.S.
application Ser. No. 15/236,238, filed on Aug. 12, 2016, and
entitled "SYSTEM AND METHOD FOR CONTENT PROVISIONING WITH DUAL
RECOMMENDATION ENGINES", now U.S. Pat. No. 10,614,368, the entirety
of each of which is hereby incorporated by reference herein.
With reference now to FIG. 1, a block diagram is shown illustrating
various components of a content distribution network (CDN) 100
which implements and supports certain embodiments and features
described herein. In some embodiments, the content distribution
network 100 can comprise one or several physical components and/or
one or several virtual components such as, for example, one or
several cloud computing components. In some embodiments, the
content distribution network 100 can comprise a mixture of physical
and cloud computing components.
Content distribution network 100 may include one or more content
management servers 102. As discussed below in more detail, content
management servers 102 may be any desired type of server including,
for example, a rack server, a tower server, a miniature server, a
blade server, a mini rack server, a mobile server, an ultra-dense
server, a super server, or the like, and may include various
hardware components, for example, a motherboard, a processing unit,
memory systems, hard drives, network interfaces, power supplies,
etc. Content management server 102 may include one or more server
farms, clusters, or any other appropriate arrangement and/or
combination of computer servers. Content management server 102 may
act according to stored instructions located in a memory subsystem
of the server 102, and may run an operating system, including any
commercially available server operating system and/or any other
operating systems discussed herein.
The content distribution network 100 may include one or more data
store servers 104, such as database servers and file-based storage
systems. The database servers 104 can access data that can be
stored on a variety of hardware components. These hardware
components can include, for example, components forming tier 0
storage, components forming tier 1 storage, components forming tier
2 storage, and/or any other tier of storage. In some embodiments,
tier 0 storage refers to storage that is the fastest tier of
storage in the database server 104, and particularly, the tier 0
storage is the fastest storage that is not RAM or cache memory. In
some embodiments, the tier 0 memory can be embodied in solid state
memory such as, for example, a solid-state drive (SSD) and/or flash
memory.
In some embodiments, the tier 1 storage refers to storage that is
one or several higher performing systems in the memory management
system, and that is relatively slower than tier 0 memory, and
relatively faster than other tiers of memory. The tier 1 memory can
be one or several hard disks that can be, for example,
high-performance hard disks. These hard disks can be one or both of
physically or communicatingly connected such as, for example, by
one or several fiber channels. In some embodiments, the one or
several disks can be arranged into a disk storage system, and
specifically can be arranged into an enterprise class disk storage
system. The disk storage system can include any desired level of
redundancy to protect data stored therein, and in one embodiment,
the disk storage system can be made with grid architecture that
creates parallelism for uniform allocation of system resources and
balanced data distribution.
In some embodiments, the tier 2 storage refers to storage that
includes one or several relatively lower performing systems in the
memory management system, as compared to the tier 1 and tier 2
storages. Thus, tier 2 memory is relatively slower than tier 1 and
tier 0 memories. Tier 2 memory can include one or several
SATA-drives or one or several NL-SATA drives.
In some embodiments, the one or several hardware and/or software
components of the database server 104 can be arranged into one or
several storage area networks (SAN), which one or several storage
area networks can be one or several dedicated networks that provide
access to data storage, and particularly that provide access to
consolidated, block level data storage. A SAN typically has its own
network of storage devices that are generally not accessible
through the local area network (LAN) by other devices. The SAN
allows access to these devices in a manner such that these devices
appear to be locally attached to the user device.
Data stores 104 may comprise stored data relevant to the functions
of the content distribution network 100. Illustrative examples of
data stores 104 that may be maintained in certain embodiments of
the content distribution network 100 are described below in
reference to FIG. 3. In some embodiments, multiple data stores may
reside on a single server 104, either using the same storage
components of server 104 or using different physical storage
components to assure data security and integrity between data
stores. In other embodiments, each data store may have a separate
dedicated data store server 104.
Content distribution network 100 also may include one or more user
devices 106 and/or supervisor devices 110. User devices 106 and
supervisor devices 110 may display content received via the content
distribution network 100, and may support various types of user
interactions with the content. User devices 106 and supervisor
devices 110 may include mobile devices such as smartphones, tablet
computers, personal digital assistants, and wearable computing
devices. Such mobile devices may run a variety of mobile operating
systems, and may be enabled for Internet, e-mail, short message
service (SMS), Bluetooth.RTM., mobile radio-frequency
identification (M-RFID), and/or other communication protocols.
Other user devices 106 and supervisor devices 110 may be general
purpose personal computers or special-purpose computing devices
including, by way of example, personal computers, laptop computers,
workstation computers, projection devices, and interactive room
display systems. Additionally, user devices 106 and supervisor
devices 110 may be any other electronic devices, such as
thin-client computers, Internet-enabled gaming systems, business or
home appliances, and/or a personal messaging devices, capable of
communicating over network(s) 120.
In different contexts of content distribution networks 100, user
devices 106 and supervisor devices 110 may correspond to different
types of specialized devices, for example, student devices and
teacher devices in an educational network, employee devices and
presentation devices in a company network, different gaming devices
in a gaming network, etc. In some embodiments, user devices 106 and
supervisor devices 110 may operate in the same physical location
107, such as a classroom or conference room. In such cases, the
devices may contain components that support direct communications
with other nearby devices, such as a wireless transceiver and
wireless communications interfaces, Ethernet sockets or other Local
Area Network (LAN) interfaces, etc. In other implementations, the
user devices 106 and supervisor devices 110 need not be used at the
same location 107, but may be used in remote geographic locations
in which each user device 106 and supervisor device 110 may use
security features and/or specialized hardware (e.g.,
hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.)
to communicate with the content management server 102 and/or other
remotely located user devices 106. Additionally, different user
devices 106 and supervisor devices 110 may be assigned different
designated roles, such as presenter devices, teacher devices,
administrator devices, or the like, and in such cases the different
devices may be provided with additional hardware and/or software
components to provide content and support user capabilities not
available to the other devices.
The content distribution network 100 also may include a privacy
server 108 that maintains private user information at the privacy
server 108 while using applications or services hosted on other
servers. For example, the privacy server 108 may be used to
maintain private data of a user within one jurisdiction even though
the user is accessing an application hosted on a server (e.g., the
content management server 102) located outside the jurisdiction. In
such cases, the privacy server 108 may intercept communications
between a user device 106 or supervisor device 110 and other
devices that include private user information. The privacy server
108 may create a token or identifier that does not disclose the
private information and may use the token or identifier when
communicating with the other servers and systems, instead of using
the user's private information.
As illustrated in FIG. 1, the content management server 102 may be
in communication with one or more additional servers, such as a
content server 112, a user data server 112, and/or an administrator
server 116. Each of these servers may include some or all of the
same physical and logical components as the content management
server(s) 102, and in some cases, the hardware and software
components of these servers 112-116 may be incorporated into the
content management server(s) 102, rather than being implemented as
separate computer servers.
Content server 112 may include hardware and software components to
generate, store, and maintain the content resources for
distribution to user devices 106 and other devices in the network
100. For example, in content distribution networks 100 used for
professional training and educational purposes, content server 112
may include data stores of training materials, presentations,
plans, syllabi, reviews, evaluations, interactive programs and
simulations, course models, course outlines, and various training
interfaces that correspond to different materials and/or different
types of user devices 106. In content distribution networks 100
used for media distribution, interactive gaming, and the like, a
content server 112 may include media content files such as music,
movies, television programming, games, and advertisements.
User data server 114 may include hardware and software components
that store and process data for multiple users relating to each
user's activities and usage of the content distribution network
100. For example, the content management server 102 may record and
track each user's system usage, including his or her user device
106, content resources accessed, and interactions with other user
devices 106. This data may be stored and processed by the user data
server 114, to support user tracking and analysis features. For
instance, in the professional training and educational contexts,
the user data server 114 may store and analyze each user's training
materials viewed, presentations attended, courses completed,
interactions, evaluation results, and the like. The user data
server 114 may also include a repository for user-generated
material, such as evaluations and tests completed by users, and
documents and assignments prepared by users. In the context of
media distribution and interactive gaming, the user data server 114
may store and process resource access data for multiple users
(e.g., content titles accessed, access times, data usage amounts,
gaming histories, user devices and device types, etc.).
Administrator server 116 may include hardware and software
components to initiate various administrative functions at the
content management server 102 and other components within the
content distribution network 100. For example, the administrator
server 116 may monitor device status and performance for the
various servers, data stores, and/or user devices 106 in the
content distribution network 100. When necessary, the administrator
server 116 may add or remove devices from the network 100, and
perform device maintenance such as providing software updates to
the devices in the network 100. Various administrative tools on the
administrator server 116 may allow authorized users to set user
access permissions to various content resources, monitor resource
usage by users and devices 106, and perform analyses and generate
reports on specific network users and/or devices (e.g., resource
usage tracking reports, training evaluations, etc.).
The content distribution network 100 may include one or more
communication networks 120. Although only a single network 120 is
identified in FIG. 1, the content distribution network 100 may
include any number of different communication networks between any
of the computer servers and devices shown in FIG. 1 and/or other
devices described herein. Communication networks 120 may enable
communication between the various computing devices, servers, and
other components of the content distribution network 100. As
discussed below, various implementations of content distribution
networks 100 may employ different types of networks 120, for
example, computer networks, telecommunications networks, wireless
networks, and/or any combination of these and/or other
networks.
The content distribution network 100 may include one or several
navigation systems or features including, for example, the Global
Positioning System ("GPS"), GALILEO, or the like, or location
systems or features including, for example, one or several
transceivers that can determine location of the one or several
components of the content distribution network 100 via, for
example, triangulation. All of these are depicted as navigation
system 122.
In some embodiments, navigation system 122 can include several
features that can communicate with one or several components of the
content distribution network 100 including, for example, with one
or several of the user devices 106 and/or with one or several of
the supervisor devices 110. In some embodiments, this communication
can include the transmission of a signal from the navigation system
122 which signal is received by one or several components of the
content distribution network 100 and can be used to determine the
location of the one or several components of the content
distribution network 100.
With reference to FIG. 2, an illustrative distributed computing
environment 200 is shown, including a computer server 202, four
client computing devices 206, and other components that may
implement certain embodiments and features described herein. In
some embodiments, the server 202 may correspond to the content
management server 102 discussed above in FIG. 1, and the client
computing devices 206 may correspond to the user devices 106.
However, the computing environment 200 illustrated in FIG. 2 may
correspond to any other combination of devices and servers
configured to implement a client-server model or other distributed
computing architecture.
Client devices 206 may be configured to receive and execute client
applications over one or more networks 220. Such client
applications may be web browser-based applications and/or
standalone software applications, such as mobile device
applications. Server 202 may be communicatively coupled with the
client devices 206 via one or more communication networks 220.
Client devices 206 may receive client applications from server 202
or from other application providers (e.g., public or private
application stores). Server 202 may be configured to run one or
more server software applications or services, for example,
web-based or cloud-based services, to support content distribution
and interaction with client devices 206. Users operating client
devices 206 may in turn utilize one or more client applications
(e.g., virtual client applications) to interact with server 202 to
utilize the services provided by these components.
Various different subsystems and/or components 204 may be
implemented on server 202. Users operating the client devices 206
may initiate one or more client applications to use services
provided by these subsystems and components. The subsystems and
components within the server 202 and client devices 206 may be
implemented in hardware, firmware, software, or combinations
thereof. Various different system configurations are possible in
different distributed computing systems 200 and content
distribution networks 100. The embodiment shown in FIG. 2 is thus
one example of a distributed computing system and is not intended
to be limiting.
Although exemplary computing environment 200 is shown with four
client computing devices 206, any number of client computing
devices may be supported. Other devices, such as specialized sensor
devices, etc., may interact with client devices 206 and/or server
202.
As shown in FIG. 2, various security and integration components 208
may be used to send and manage communications between the server
202 and user devices 206 over one or more communication networks
220. The security and integration components 208 may include
separate servers, such as web servers and/or authentication
servers, and/or specialized networking components, such as
firewalls, routers, gateways, load balancers, and the like. In some
cases, the security and integration components 208 may correspond
to a set of dedicated hardware and/or software operating at the
same physical location and under the control of same entities as
server 202. For example, components 208 may include one or more
dedicated web servers and network hardware in a data center or a
cloud infrastructure. In other examples, the security and
integration components 208 may correspond to separate hardware and
software components which may be operated at a separate physical
location and/or by a separate entity.
Security and integration components 208 may implement various
security features for data transmission and storage, such as
authenticating users and restricting access to unknown or
unauthorized users. In various implementations, security and
integration components 208 may provide, for example, a file-based
integration scheme or a service-based integration scheme for
transmitting data between the various devices in the content
distribution network 100. Security and integration components 208
also may use secure data transmission protocols and/or encryption
for data transfers, for example, File Transfer Protocol (FTP),
Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy
(PGP) encryption.
In some embodiments, one or more web services may be implemented
within the security and integration components 208 and/or elsewhere
within the content distribution network 100. Such web services,
including cross-domain and/or cross-platform web services, may be
developed for enterprise use in accordance with various web service
standards, such as RESTful web services (i.e., services based on
the Representation State Transfer (REST) architectural style and
constraints), and/or web services designed in accordance with the
Web Service Interoperability (WS-I) guidelines. Some web services
may use the Secure Sockets Layer (SSL) or Transport Layer Security
(TLS) protocol to provide secure connections between the server 202
and user devices 206. SSL or TLS may use HTTP or HTTPS to provide
authentication and confidentiality. In other examples, web services
may be implemented using REST over HTTPS with the OAuth open
standard for authentication, or using the WS-Security standard
which provides for secure SOAP messages using XML encryption. In
other examples, the security and integration components 208 may
include specialized hardware for providing secure web services. For
example, security and integration components 208 may include secure
network appliances having built-in features such as
hardware-accelerated SSL and HTTPS, WS-Security, and firewalls.
Such specialized hardware may be installed and configured in front
of any web servers, so that any external devices may communicate
directly with the specialized hardware.
Communication network(s) 220 may be any type of network familiar to
those skilled in the art that can support data communications using
any of a variety of commercially-available protocols, including
without limitation, TCP/IP (transmission control protocol/Internet
protocol), SNA (systems network architecture), IPX (Internet packet
exchange), Secure Sockets Layer (SSL) or Transport Layer Security
(TLS) protocols, Hyper Text Transfer Protocol (HTTP) and Secure
Hyper Text Transfer Protocol (HTTPS), Bluetooth.RTM., Near Field
Communication (NFC), and the like. Merely by way of example,
network(s) 220 may be local area networks (LAN), such as one based
on Ethernet, Token-Ring and/or the like. Network(s) 220 also may be
wide-area networks, such as the Internet. Networks 220 may include
telecommunication networks such as public switched telephone
networks (PSTNs), or virtual networks such as an intranet or an
extranet. Infrared and wireless networks (e.g., using the Institute
of Electrical and Electronics (IEEE) 802.11 protocol suite or other
wireless protocols) also may be included in networks 220.
Computing environment 200 also may include one or more data stores
210 and/or back-end servers 212. In certain examples, the data
stores 210 may correspond to data store server(s) 104 discussed
above in FIG. 1, and back-end servers 212 may correspond to the
various back-end servers 112-116. Data stores 210 and servers 212
may reside in the same datacenter or may operate at a remote
location from server 202. In some cases, one or more data stores
210 may reside on a non-transitory storage medium within the server
202. Other data stores 210 and back-end servers 212 may be remote
from server 202 and configured to communicate with server 202 via
one or more networks 220. In certain embodiments, data stores 210
and back-end servers 212 may reside in a storage-area network
(SAN), or may use storage-as-a-service (STaaS) architectural
model.
With reference to FIG. 3, an illustrative set of data stores and/or
data store servers is shown, corresponding to the data store
servers 104 of the content distribution network 100 discussed above
in FIG. 1. One or more individual data stores 301-311 may reside in
storage on a single computer server 104 (or a single server farm or
cluster) under the control of a single entity, or may reside on
separate servers operated by different entities and/or at remote
locations. In some embodiments, data stores 301-311 may be accessed
by the content management server 102 and/or other devices and
servers within the network 100 (e.g., user devices 106, supervisor
devices 110, administrator servers 116, etc.). Access to one or
more of the data stores 301-311 may be limited or denied based on
the processes, user credentials, and/or devices attempting to
interact with the data store.
The paragraphs below describe examples of specific data stores that
may be implemented within some embodiments of a content
distribution network 100. It should be understood that the below
descriptions of data stores 301-311, including their functionality
and types of data stored therein, are illustrative and
non-limiting. Data stores server architecture, design, and the
execution of specific data stores 301-311 may depend on the
context, size, and functional requirements of a content
distribution network 100. For example, in content distribution
systems 100 used for professional training and educational
purposes, separate databases or file-based storage systems may be
implemented in data store server(s) 104 to store trainee and/or
student data, trainer and/or professor data, training module data
and content descriptions, training results, evaluation data, and
the like. In contrast, in content distribution systems 100 used for
media distribution from content providers to subscribers, separate
data stores may be implemented in data stores server(s) 104 to
store listings of available content titles and descriptions,
content title usage statistics, subscriber profiles, account data,
payment data, network usage statistics, etc.
A user profile data store 301, also referred to herein as a user
profile database 301, may include information, which can include
user metadata, relating to the end users within the content
distribution network 100. As used herein, the user metadata
identifies data that can be stored in the user profile database 301
and that identifies one or several attributes of the user such as,
for example, a user skill level, learning preference, or the like.
This information stored in the user profile database 301 may
include user characteristics such as the user names, access
credentials (e.g., logins and passwords), user preferences, and
information relating to any previous user interactions within the
content distribution network 100 (e.g., requested content, posted
content, content modules completed, training scores or evaluations,
other associated users, etc.). In some embodiments, this
information can relate to one or several individual end users such
as, for example, one or several students, teachers, administrators,
or the like, and in some embodiments, this information can relate
to one or several institutional end users such as, for example, one
or several schools, groups of schools such as one or several school
districts, one or several colleges, one or several universities,
one or several training providers, or the like. In some
embodiments, this information can identify one or several user
memberships in one or several groups such as, for example, a
student's membership in a university, school, program, grade,
course, class, or the like.
The user profile database 301 can include information relating to a
user's status, location, or the like. This information can
identify, for example, a device a user is using, the location of
that device, or the like. In some embodiments, this information can
be generated based on any location detection technology including,
for example, a navigation system 122, or the like.
Information relating to the user's status can identify, for
example, logged-in status information that can indicate whether the
user is presently logged-in to the content distribution network 100
and/or whether the log-in-is active. In some embodiments, the
information relating to the user's status can identify whether the
user is currently accessing content and/or participating in an
activity from the content distribution network 100.
In some embodiments, information relating to the user's status can
identify, for example, one or several attributes of the user's
interaction with the content distribution network 100, and/or
content distributed by the content distribution network 100. This
can include data identifying the user's interactions with the
content distribution network 100, the content consumed by the user
through the content distribution network 100, or the like. In some
embodiments, this can include data identifying the type of
information accessed through the content distribution network 100
and/or the type of activity performed by the user via the content
distribution network 100, the lapsed time since the last time the
user accessed content and/or participated in an activity from the
content distribution network 100, or the like. In some embodiments,
this information can relate to a content program comprising an
aggregate of data, content, and/or activities, and can identify,
for example, progress through the content program, or through the
aggregate of data, content, and/or activities forming the content
program. In some embodiments, this information can track, for
example, the amount of time since participation in and/or
completion of one or several types of activities, the amount of
time since communication with one or several supervisors and/or
supervisor devices 110, or the like.
In some embodiments in which the one or several end users are
individuals, and specifically are students, the user profile
database 301 can further include information relating to these
students' academic and/or educational history. This information can
identify one or several courses of study that the student has
initiated, completed, and/or partially completed, as well as grades
received in those courses of study. In some embodiments, the
student's academic and/or educational history can further include
information identifying student performance on one or several
tests, quizzes, and/or assignments. In some embodiments, this
information can be stored in a tier of memory that is not the
fastest memory in the content delivery network 100.
The user profile database 301 can include information relating to
one or several student learning preferences. In some embodiments,
for example, the user, also referred to herein as the students or
the student-user may have one or several preferred learning styles,
one or several more effective learning styles, and/or the like. In
some embodiments, the student's learning style can be any learning
style describing how the student best learns or how the student
prefers to learn. In one embodiment, these learning styles can
include, for example, identification of the student as an auditory
learner, as a visual learner, and/or as a tactile learner. In some
embodiments, the data identifying one or several student learning
styles can include data identifying a learning style based on the
student's educational history, such as, for example, identifying a
student as an auditory learner when the student has received
significantly higher grades and/or scores on assignments and/or in
courses favorable to auditory learners. In some embodiments, this
information can be stored in a tier of memory that is not the
fastest memory in the content delivery network 100.
In some embodiments, the user profile data store 301 can further
include information identifying one or several user skill levels.
In some embodiments, these one or several user skill levels can
identify a skill level determined based on past performance by the
user interacting with the content delivery network 100, and in some
embodiments, these one or several user skill levels can identify a
predicted skill level determined based on past performance by the
user interacting with the content delivery network 100 and one or
several predictive models.
The user profile database 301 can further include information
relating to one or several teachers and/or instructors who are
responsible for organizing, presenting, and/or managing the
presentation of information to the student. In some embodiments,
user profile database 301 can include information identifying
courses and/or subjects that have been taught by the teacher, data
identifying courses and/or subjects currently taught by the
teacher, and/or data identifying courses and/or subjects that will
be taught by the teacher. In some embodiments, this can include
information relating to one or several teaching styles of one or
several teachers. In some embodiments, the user profile database
301 can further include information indicating past evaluations
and/or evaluation reports received by the teacher. In some
embodiments, the user profile database 301 can further include
information relating to improvement suggestions received by the
teacher, training received by the teacher, continuing education
received by the teacher, and/or the like. In some embodiments, this
information can be stored in a tier of memory that is not the
fastest memory in the content delivery network 100.
An accounts data store 302, also referred to herein as an account
database 302, may generate and store account data for different
users in various roles within the content distribution network 100.
For example, accounts may be created in an account data store 302
for individual end users, supervisors, administrator users, and
entities such as companies or educational institutions. Account
data may include account types, current account status, account
characteristics, and any parameters, limits, restrictions
associated with the accounts.
A content library data store 303, also referred to herein as a
content library database 303 or a data packet database 303, may
include information describing the individual content items (or
content resources or data packets) available via the content
distribution network 100. In some embodiments, these data packets
in the content library database 303 can be linked to form an object
network. In some embodiments, these data packets can be linked in
the object network according to one or several prerequisite
relationships that can, for example, identify the relative
hierarchy and/or difficulty of the data objects. In some
embodiments, this hierarchy of data objects can be generated by the
content distribution network 100 according to user experience with
the object network, and in some embodiments, this hierarchy of data
objects can be generated based on one or several existing and/or
external hierarchies such as, for example, a syllabus, a table of
contents, or the like. In some embodiments, for example, the object
network can correspond to a syllabus such that content for the
syllabus is embodied in the object network.
In some embodiments, the content library data store 303 can
comprise a syllabus, a schedule, or the like. In some embodiments,
the syllabus or schedule can identify one or several tasks and/or
events relevant to the user. In some embodiments, for example, when
the user is a member of a group such as a section or a class, these
tasks and/or events relevant to the user can identify one or
several assignments, quizzes, exams, or the like.
In some embodiments, the library data store 303 may include
metadata, properties, and other characteristics associated with the
content resources stored in the content server 112. Such data may
identify one or more aspects or content attributes of the
associated content resources, for example, subject matter, access
level, or skill level of the content resources, license attributes
of the content resources (e.g., any limitations and/or restrictions
on the licensable use and/or distribution of the content resource),
price attributes of the content resources (e.g., a price and/or
price structure for determining a payment amount for use or
distribution of the content resource), rating attributes for the
content resources (e.g., data indicating the evaluation or
effectiveness of the content resource), and the likes. In some
embodiments, the library data store 303 may be configured to allow
updating of content metadata or properties, and to allow the
addition and/or removal of information relating to the content
resources. For example, content relationships may be implemented as
graph structures, which may be stored in the library data store 303
or in an additional store for use by selection algorithms along
with the other metadata.
In some embodiments, the content library data store 303 can contain
information used in evaluating the responses received from users.
In some embodiments, for example, a user can receive content from
the content distribution network 100 and can, subsequent to
receiving that content, provide a response to the received content.
In some embodiments, for example, the received content can comprise
one or several questions, prompts, or the like, and the response to
the received content can comprise an answer to those one or several
questions, prompts, or the like. In some embodiments, information,
referred to herein as "comparative data," from the content library
data store 303 can be used to determine whether the responses are
the correct and/or desired responses.
In some embodiments, the content library database 303 and/or the
user profile database 301 can comprise an aggregation network, also
referred to herein as a content network or content aggregation
network. The aggregation network can comprise a plurality of
content aggregations that can be linked together by, for example:
creation by common user; relation to a common subject, topic,
skill, or the like; creation from a common set of source material
such as source data packets; or the like. In some embodiments, the
content aggregation can comprise a grouping of content comprising
the presentation portion that can be provided to the user in the
form of, for example, a flash card and an extraction portion that
can comprise the desired response to the presentation portion such
as, for example, an answer to a flash card. In some embodiments,
one or several content aggregations can be generated by the content
distribution network 100 and can be related to one or several data
packets that they can be, for example, organized in object network.
In some embodiments, the one or several content aggregations can be
each created from content stored in one or several of the data
packets.
In some embodiments, the content aggregations located in the
content library database 303 and/or the user profile database 301
can be associated with a user-creator of those content
aggregations. In some embodiments, access to content aggregations
can vary based on, for example, whether a user created the content
aggregations. In some embodiments, the content library database 303
and/or the user profile database 301 can comprise a database of
content aggregations associated with a specific user, and in some
embodiments, the content library database 303 and/or the user
profile database 301 can comprise a plurality of databases of
content aggregations that are each associated with a specific user.
In some embodiments, these databases of content aggregations can
include content aggregations created by their specific user and, in
some embodiments, these databases of content aggregations can
further include content aggregations selected for inclusion by
their specific user and/or a supervisor of that specific user. In
some embodiments, these content aggregations can be arranged and/or
linked in a hierarchical relationship similar to the data packets
in the object network and/or linked to the object network in the
object network or the tasks or skills associated with the data
packets in the object network or the syllabus or schedule.
In some embodiments, the content aggregation network, and the
content aggregations forming the content aggregation network can be
organized according to the object network and/or the hierarchical
relationships embodied in the object network. In some embodiments,
the content aggregation network, and/or the content aggregations
forming the content aggregation network can be organized according
to one or several tasks identified in the syllabus, schedule or the
like.
A pricing data store 304 may include pricing information and/or
pricing structures for determining payment amounts for providing
access to the content distribution network 100 and/or the
individual content resources within the network 100. In some cases,
pricing may be determined based on a user's access to the content
distribution network 100, for example, a time-based subscription
fee, or pricing based on network usage. In other cases, pricing may
be tied to specific content resources. Certain content resources
may have associated pricing information, whereas other pricing
determinations may be based on the resources accessed, the profiles
and/or accounts of the user, and the desired level of access (e.g.,
duration of access, network speed, etc.). Additionally, the pricing
data store 304 may include information relating to compilation
pricing for groups of content resources, such as group prices
and/or price structures for groupings of resources.
A license data store 305 may include information relating to
licenses and/or licensing of the content resources within the
content distribution network 100. For example, the license data
store 305 may identify licenses and licensing terms for individual
content resources and/or compilations of content resources in the
content server 112, the rights holders for the content resources,
and/or common or large-scale right holder information such as
contact information for rights holders of content not included in
the content server 112.
A content access data store 306 may include access rights and
security information for the content distribution network 100 and
specific content resources. For example, the content access data
store 306 may include login information (e.g., user identifiers,
logins, passwords, etc.) that can be verified during user login
attempts to the network 100. The content access data store 306 also
may be used to store assigned user roles and/or user levels of
access. For example, a user's access level may correspond to the
sets of content resources and/or the client or server applications
that the user is permitted to access. Certain users may be
permitted or denied access to certain applications and resources
based on their subscription level, training program, course/grade
level, etc. Certain users may have supervisory access over one or
more end users, allowing the supervisor to access all or portions
of the end user's content, activities, evaluations, etc.
Additionally, certain users may have administrative access over
some users and/or some applications in the content management
network 100, allowing such users to add and remove user accounts,
modify user access permissions, perform maintenance updates on
software and servers, etc.
A source data store 307 may include information relating to the
source of the content resources available via the content
distribution network. For example, a source data store 307 may
identify the authors and originating devices of content resources,
previous pieces of data and/or groups of data originating from the
same authors or originating devices, and the like.
An evaluation data store 308 may include information used to direct
the evaluation of users and content resources in the content
management network 100. In some embodiments, the evaluation data
store 308 may contain, for example, the analysis criteria and the
analytical guidelines for evaluating users (e.g.,
trainees/students, gaming users, media content consumers, etc.)
and/or for evaluating the content resources in the network 100. The
evaluation data store 308 also may include information relating to
evaluation processing tasks, for example, the identification of
users and user devices 106 that have received certain content
resources or accessed certain applications, the status of
evaluations or evaluation histories for content resources, users,
or applications, and the like. Evaluation criteria may be stored in
the evaluation data store 308 including data and/or instructions in
the form of one or several electronic rubrics or scoring guides for
use in the evaluation of the content, users, or applications. The
evaluation data store 308 also may include past evaluations and/or
evaluation analyses for users, content, and applications, including
relative rankings, characterizations, explanations, and the
like.
A model data store 309, also referred to herein as a model database
309, can store information relating to one or several predictive
models. In some embodiments, these can include one or several
evidence models, risk models, skill models, or the like. In some
embodiments, an evidence model can be a mathematically-based
statistical model. The evidence model can be based on, for example,
Item Response Theory (IRT), Bayesian Network (Bayes net),
Performance Factor Analysis (PFA), or the like. The evidence model
can, in some embodiments, be customizable to a user and/or to one
or several content items. Specifically, one or several inputs
relating to the user and/or to one or several content items can be
inserted into the evidence model. These inputs can include, for
example, one or several measures of user skill level, one or
several measures of content item difficulty and/or skill level, or
the like. The customized evidence model can then be used to predict
the probability of the user providing desired or undesired
responses to one or several of the content items.
In some embodiments, the risk models can include one or several
models that can be used to calculate one or several model function
values. In some embodiments, these one or several model function
values can be used to calculate a risk probability, which risk
probability can characterize the risk of a user such as a
student-user failing to achieve a desired outcome such as, for
example, failing to correctly respond to one or several data
packets, failure to achieve a desired level of completion of a
program, for example in a pre-defined time period, failure to
achieve a desired learning outcome, or the like. In some
embodiments, the risk probability can identify the risk of the
student-user failing to complete 60% of the program.
In some embodiments, these models can include a plurality of model
functions including, for example, a first model function, a second
model function, a third model function, and a fourth model
function. In some embodiments, some or all of the model functions
can be associated with a portion of the program such as, for
example, a completion stage and/or completion status of the
program. In one embodiment, for example, the first model function
can be associated with a first completion status, the second model
function can be associated with a second completion status, the
third model function can be associated with a third completion
status, and the fourth model function can be associated with a
fourth completion status. In some embodiments, these completion
statuses can be selected such that some or all of these completion
statuses are less than the desired level of completion of the
program. Specifically, in some embodiments, these completion
statuses can be selected to all be at less than 60% completion of
the program, and more specifically, in some embodiments, the first
completion status can be at 20% completion of the program, the
second completion status can be at 30% completion of the program,
the third completion status can be at 40% completion of the
program, and the fourth completion status can be at 50% completion
of the program. Similarly, any desired number of model functions
can be associated with any desired number of completion
statuses.
In some embodiments, a model function can be selected from the
plurality of model functions based on a student-user's progress
through a program. In some embodiments, the student-user's progress
can be compared to one or several status trigger thresholds, each
of which status trigger thresholds can be associated with one or
more of the model functions. If one of the status triggers is
triggered by the student-user's progress, the corresponding one or
several model functions can be selected.
The model functions can comprise a variety of types of models
and/or functions. In some embodiments, each of the model functions
outputs a function value that can be used in calculating a risk
probability. This function value can be calculated by performing
one or several mathematical operations on one or several values
indicative of one or several user attributes and/or user
parameters, also referred to herein as program status parameters.
In some embodiments, each of the model functions can use the same
program status parameters, and in some embodiments, the model
functions can use different program status parameters. In some
embodiments, the model functions use different program status
parameters when at least one of the model functions uses at least
one program status parameter that is not used by others of the
model functions.
In some embodiments, a skill model can comprise a statistical model
identifying a predictive skill level of one or several students. In
some embodiments, this model can identify a single skill level of a
student and/or a range of possible skill levels of a student. In
some embodiments, this statistical model can identify a skill level
of a student-user and an error value or error range associated with
that skill level. In some embodiments, the error value can be
associated with a confidence interval determined based on a
confidence level. Thus, in some embodiments, as the number of
student interactions with the content distribution network
increases, the confidence level can increase and the error value
can decrease such that the range identified by the error value
about the predicted skill level is smaller.
In some embodiments, the model database 309 can comprise a
weighting database that can be, for example, a sub-database of the
model database 309. In some embodiments, the weighting database can
include information identifying how to weight one or several data
packets based on a probability associated with each of those data
packets. In some embodiments, this information in the weighing
database, also referred to herein as weighting data, can include
one or several weights, numerical data, a curve, a function, or the
like.
A threshold database 310, also referred to herein as a threshold
database, can store one or several threshold values. These one or
several threshold values can delineate between states or
conditions. In one exemplary embodiment, for example, a threshold
value can delineate between an acceptable user performance and an
unacceptable user performance, between content appropriate for a
user and content that is inappropriate for a user, between risk
levels, or the like.
In addition to the illustrative data stores described above, data
store server(s) 104 (e.g., database servers, file-based storage
servers, etc.) may include one or more external data aggregators
311. External data aggregators 311 may include third-party data
sources accessible to the content management network 100, but not
maintained by the content management network 100. External data
aggregators 311 may include any electronic information source
relating to the users, content resources, or applications of the
content distribution network 100. For example, external data
aggregators 311 may be third-party data stores containing
demographic data, education-related data, consumer sales data,
health-related data, and the like. Illustrative external data
aggregators 311 may include, for example, social networking web
servers, public records data stores, learning management systems,
educational institution servers, business servers, consumer sales
data stores, medical record data stores, etc. Data retrieved from
various external data aggregators 311 may be used to verify and
update user account information, suggest user content, and perform
user and content evaluations.
With reference now to FIG. 4, a block diagram is shown illustrating
an embodiment of one or more content management servers 102 within
a content distribution network 100. In such an embodiment, content
management server 102 performs internal data gathering and
processing of streamed content along with external data gathering
and processing. Other embodiments could have either all external or
all internal data gathering. This embodiment allows reporting
timely information that might be of interest to the reporting party
or other parties. In this embodiment, the content management server
102 can monitor gathered information from several sources to allow
it to make timely business and/or processing decisions based upon
that information. For example, reports of user actions and/or
responses, as well as the status and/or results of one or several
processing tasks could be gathered and reported to the content
management server 102 from a number of sources.
Internally, the content management server 102 gathers information
from one or more internal components 402-408. The internal
components 402-408 gather and/or process information relating to
such things as: content provided to users; content consumed by
users; responses provided by users; user skill levels; content
difficulty levels; next content for providing to users; etc. The
internal components 402-408 can report the gathered and/or
generated information in real-time, near real-time or along another
time line. To account for any delay in reporting information, a
time stamp or staleness indicator can inform others of how timely
the information was sampled. The content management server 102 can
opt to allow third parties to use internally or externally gathered
information that is aggregated within the server 102 by
subscription to the content distribution network 100.
A command and control (CC) interface 338 configures the gathered
input information to an output of data streams, also referred to
herein as content streams. APIs for accepting gathered information
and providing data streams are provided to third parties external
to the server 102 who want to subscribe to data streams. The server
102 or a third party can design as yet undefined APIs using the CC
interface 338. The server 102 can also define authorization and
authentication parameters using the CC interface 338 such as
authentication, authorization, login, and/or data encryption. CC
information is passed to the internal components 402-408 and/or
other components of the content distribution network 100 through a
channel separate from the gathered information or data stream in
this embodiment, but other embodiments could embed CC information
in these communication channels. The CC information allows
throttling information reporting frequency, specifying formats for
information and data streams, deactivation of one or several
internal components 402-408 and/or other components of the content
distribution network 100, updating authentication and
authorization, etc.
The various data streams that are available can be researched and
explored through the CC interface 338. Those data stream selections
for a particular subscriber, which can be one or several of the
internal components 402-408 and/or other components of the content
distribution network 100, are stored in the queue subscription
information database 322. The server 102 and/or the CC interface
338 then routes selected data streams to processing subscribers
that have selected delivery of a given data stream. Additionally,
the server 102 also supports historical queries of the various data
streams that are stored in an historical data store 334 as gathered
by an archive data agent 336. Through the CC interface 238 various
data streams can be selected for archiving into the historical data
store 334.
Components of the content distribution network 100 outside of the
server 102 can also gather information that is reported to the
server 102 in real-time, near real-time or along another time line.
There is a defined API between those components and the server 102.
Each type of information or variable collected by server 102 falls
within a defined API or multiple APIs. In some cases, the CC
interface 338 is used to define additional variables to modify an
API that might be of use to processing subscribers. The additional
variables can be passed to all processing subscribes or just a
subset. For example, a component of the content distribution
network 100 outside of the server 102 may report a user response
but define an identifier of that user as a private variable that
would not be passed to processing subscribers lacking access to
that user and/or authorization to receive that user data.
Processing subscribers having access to that user and/or
authorization to receive that user data would receive the
subscriber identifier along with response reported that component.
Encryption and/or unique addressing of data streams or sub-streams
can be used to hide the private variables within the messaging
queues.
The user devices 106 and/or supervisor devices 110 communicate with
the server 102 through security and/or integration hardware 410.
The communication with security and/or integration hardware 410 can
be encrypted or not. For example, a socket using a TCP connection
could be used. In addition to TCP, other transport layer protocols
like SCTP and UDP could be used in some embodiments to intake the
gathered information. A protocol such as SSL could be used to
protect the information over the TCP connection. Authentication and
authorization can be performed to any user devices 106 and/or
supervisor device interfacing to the server 102. The security
and/or integration hardware 410 receives the information from one
or several of the user devices 106 and/or the supervisor devices
110 by providing the API and any encryption, authorization, and/or
authentication. In some cases, the security and/or integration
hardware 410 reformats or rearranges this received information.
The messaging bus 412, also referred to herein as a messaging queue
or a messaging channel, can receive information from the internal
components of the server 102 and/or components of the content
distribution network 100 outside of the server 102 and distribute
the gathered information as a data stream to any processing
subscribers that have requested the data stream from the messaging
queue 412. Specifically, in some embodiments, the messaging bus 412
can receive and output information from at least one of the packet
selection system, the presentation system, the response system, and
the summary model system. In some embodiments, this information can
be output according to a "push" model, and in some embodiments,
this information can be output according to a "pull" model.
As indicated in FIG. 4, processing subscribers are indicated by a
connector to the messaging bus 412, the connector having an arrow
head pointing away from the messaging bus 412. Only data streams
within the messaging queue 412 that a particular processing
subscriber has subscribed to may be read by that processing
subscriber if received at all. Gathered information sent to the
messaging queue 412 is processed and returned in a data stream in a
fraction of a second by the messaging queue 412. Various
multicasting and routing techniques can be used to distribute a
data stream from the messaging queue 412 that a number of
processing subscribers have requested. Protocols such as Multicast
or multiple Unicast could be used to distribute streams within the
messaging queue 412. Additionally, transport layer protocols like
TCP, SCTP and UDP could be used in various embodiments.
Through the CC interface 338, an external or internal processing
subscriber can be assigned one or more data streams within the
messaging queue 412. A data stream is a particular type of message
in a particular category. For example, a data stream can comprise
all of the data reported to the messaging bus 412 by a designated
set of components. One or more processing subscribers could
subscribe and receive the data stream to process the information
and make a decision and/or feed the output from the processing as
gathered information fed back into the messaging queue 412. Through
the CC interface 338 a developer can search the available data
streams or specify a new data stream and its API. The new data
stream might be determined by processing a number of existing data
streams with a processing subscriber.
The CDN 110 has internal processing subscribers 402-408 that
process assigned data streams to perform functions within the
server 102. Internal processing subscribers 402-408 could perform
functions such as providing content to a user, receiving a response
from a user, determining the correctness of the received response,
updating one or several models based on the correctness of the
response, recommending new content for providing to one or several
users, or the like. The internal processing subscribers 402-408 can
decide filtering and weighting of records from the data stream. To
the extent that decisions are made based upon analysis of the data
stream, each data record is time stamped to reflect when the
information was gathered such that additional credibility could be
given to more recent results, for example. Other embodiments may
filter out records in the data stream that are from an unreliable
source or stale. For example, a particular contributor of
information may prove to have less than optimal gathered
information and that could be weighted very low or removed
altogether.
Internal processing subscribers 402-408 may additionally process
one or more data streams to provide different information to feed
back into the messaging queue 412 to be part of a different data
stream. For example, hundreds of user devices 106 could provide
responses that are put into a data stream on the messaging queue
412. An internal processing subscriber 402-408 could receive the
data stream and process it to determine the difficulty of one or
several data packets provided to one or several users, and supply
this information back onto the messaging queue 412 for possible use
by other internal and external processing subscribers.
As mentioned above, the CC interface 338 allows the CDN 110 to
query historical messaging queue 412 information. An archive data
agent 336 listens to the messaging queue 412 to store data streams
in a historical database 334. The historical database 334 may store
data streams for varying amounts of time and may not store all data
streams. Different data streams may be stored for different amounts
of time.
With regard to the components 402-48, the content management
server(s) 102 may include various server hardware and software
components that manage the content resources within the content
distribution network 100 and provide interactive and adaptive
content to users on various user devices 106. For example, content
management server(s) 102 may provide instructions to and receive
information from the other devices within the content distribution
network 100, in order to manage and transmit content resources,
user data, and server or client applications executing within the
network 100.
A content management server 102 may include a packet selection
system 402. The packet selection system 402 may be implemented
using dedicated hardware within the content distribution network
100 (e.g., a packet selection server 402), or using designated
hardware and software resources within a shared content management
server 102. In some embodiments, the packet selection system 402
may adjust the selection and adaptive capabilities of content
resources to match the needs and desires of the users receiving the
content. For example, the packet selection system 402 may query
various data stores and servers 104 to retrieve user information,
such as user preferences and characteristics (e.g., from a user
profile data store 301), user access restrictions to content
recourses (e.g., from a content access data store 306), previous
user results and content evaluations (e.g., from an evaluation data
store 308), and the like. Based on the retrieved information from
data stores 104 and other data sources, the packet selection system
402 may modify content resources for individual users.
In some embodiments, the packet selection system 402 can include a
recommendation engine, also referred to herein as an adaptive
recommendation engine. In some embodiments, the recommendation
engine can select one or several pieces of content, also referred
to herein as data packets, for providing to a user. These data
packets can be selected based on, for example, the information
retrieved from the database server 104 including, for example, the
user profile database 301, the content library database 303, the
model database 309, or the like. In some embodiments, these one or
several data packets can be adaptively selected and/or selected
according to one or several selection rules. In one embodiment, for
example, the recommendation engine can retrieve information from
the user profile database 301 identifying, for example, a skill
level of the user. The recommendation engine can further retrieve
information from the content library database 303 identifying, for
example, potential data packets for providing to the user and the
difficulty of those data packets and/or the skill level associated
with those data packets.
The recommendation engine can identify one or several potential
data packets for providing and/or one or several data packets for
providing to the user based on, for example, one or several rules,
models, predictions, or the like. The recommendation engine can use
the skill level of the user to generate a prediction of the
probability of one or several users providing a desired response to
some or all of the potential data packets. In some embodiments, the
recommendation engine can pair one or several data packets with
selection criteria that may be used to determine which packet
should be delivered to a student-user based on one or several
received responses from that student-user. In some embodiments, one
or several data packets can be eliminated from the pool of
potential data packets if the prediction indicates either too high
a probability of a desired response or too low a probability of a
desired response. In some embodiments, the recommendation engine
can then apply one or several selection criteria to the remaining
potential data packets to select a data packet for providing to the
user. These one or several selection criteria can be based on, for
example, criteria relating to a desired estimated time for receipt
of response to the data packet, one or several content parameters,
one or several assignment parameters, or the like.
A content management server 102 also may include a summary model
system 404. The summary model system 404 may be implemented using
dedicated hardware within the content distribution network 100
(e.g., a summary model server 404), or using designated hardware
and software resources within a shared content management server
102. In some embodiments, the summary model system 404 may monitor
the progress of users through various types of content resources
and groups, such as media compilations, courses or curriculums in
training or educational contexts, interactive gaming environments,
and the like. For example, the summary model system 404 may query
one or more databases and/or data store servers 104 to retrieve
user data such as associated content compilations or programs,
content completion status, user goals, results, and the like.
A content management server 102 also may include a response system
406, which can include, in some embodiments, a response processor.
The response system 406 may be implemented using dedicated hardware
within the content distribution network 100 (e.g., a response
server 406), or using designated hardware and software resources
within a shared content management server 102. The response system
406 may be configured to receive and analyze information from user
devices 106. For example, various ratings of content resources
submitted by users may be compiled and analyzed, and then stored in
a data store (e.g., a content library data store 303 and/or
evaluation data store 308) associated with the content. In some
embodiments, the response server 406 may analyze the information to
determine the effectiveness or appropriateness of content resources
with, for example, a subject matter, an age group, a skill level,
or the like. In some embodiments, the response system 406 may
provide updates to the packet selection system 402 or the summary
model system 404, with the attributes of one or more content
resources or groups of resources within the network 100. The
response system 406 also may receive and analyze user evaluation
data from user devices 106, supervisor devices 110, and
administrator servers 116, etc. For instance, response system 406
may receive, aggregate, and analyze user evaluation data for
different types of users (e.g., end users, supervisors,
administrators, etc.) in different contexts (e.g., media consumer
ratings, trainee or student comprehension levels, teacher
effectiveness levels, gamer skill levels, etc.).
In some embodiments, the response system 406 can be further
configured to receive one or several responses from the user and
analyze these one or several responses. In some embodiments, for
example, the response system 406 can be configured to translate the
one or several responses into one or several observables. As used
herein, an observable is a characterization of a received response.
In some embodiments, the translation of the one or several
responses into one or several observables can include determining
whether the one or several responses are correct responses, also
referred to herein as desired responses, or are incorrect
responses, also referred to herein as undesired responses. In some
embodiments, the translation of the one or several responses into
one or several observables can include characterizing the degree to
which one or several responses are desired responses and/or
undesired responses. In some embodiments, one or several values can
be generated by the response system 406 to reflect user performance
in responding to the one or several data packets. In some
embodiments, these one or several values can comprise one or
several scores for one or several responses and/or data
packets.
A content management server 102 also may include a presentation
system 408. The presentation system 408 may be implemented using
dedicated hardware within the content distribution network 100
(e.g., a presentation server 408), or using designated hardware and
software resources within a shared content management server 102.
The presentation system 408 can include a presentation engine that
can be, for example, a software module running on the content
delivery system.
The presentation system 408, also referred to herein as the
presentation module or the presentation engine, may receive content
resources from the packet selection system 402 and/or from the
summary model system 404, and provide the resources to user devices
106. The presentation system 408 may determine the appropriate
presentation format for the content resources based on the user
characteristics and preferences, and/or the device capabilities of
user devices 106. If needed, the presentation system 408 may
convert the content resources to the appropriate presentation
format and/or compress the content before transmission. In some
embodiments, the presentation system 408 may also determine the
appropriate transmission media and communication protocols for
transmission of the content resources.
In some embodiments, the presentation system 408 may include
specialized security and integration hardware 410, along with
corresponding software components to implement the appropriate
security features content transmission and storage, to provide the
supported network and client access models, and to support the
performance and scalability requirements of the network 100. The
security and integration layer 410 may include some or all of the
security and integration components 208 discussed above in FIG. 2,
and may control the transmission of content resources and other
data, as well as the receipt of requests and content interactions,
to and from the user devices 106, supervisor devices 110,
administrative servers 116, and other devices in the network
100.
With reference now to FIG. 5, a block diagram of an illustrative
computer system is shown. The system 500 may correspond to any of
the computing devices or servers of the content distribution
network 100 described above, or any other computing devices
described herein, and specifically can include, for example, one or
several of the user devices 106, the supervisor device 110, and/or
any of the servers 102, 104, 108, 112, 114, 116. In this example,
computer system 500 includes processing units 504 that communicate
with a number of peripheral subsystems via a bus subsystem 502.
These peripheral subsystems include, for example, a storage
subsystem 510, an I/O subsystem 526, and a communications subsystem
532.
Bus subsystem 502 provides a mechanism for letting the various
components and subsystems of computer system 500 communicate with
each other as intended. Although bus subsystem 502 is shown
schematically as a single bus, alternative embodiments of the bus
subsystem may utilize multiple buses. Bus subsystem 502 may be any
of several types of bus structures including a memory bus or memory
controller, a peripheral bus, and a local bus using any of a
variety of bus architectures. Such architectures may include, for
example, an Industry Standard Architecture (ISA) bus, Micro Channel
Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics
Standards Association (VESA) local bus, and Peripheral Component
Interconnect (PCI) bus, which can be implemented as a Mezzanine bus
manufactured to the IEEE P1386.1 standard.
Processing unit 504, which may be implemented as one or more
integrated circuits (e.g., a conventional microprocessor or
microcontroller), controls the operation of computer system 500.
One or more processors, including single core and/or multicore
processors, may be included in processing unit 504. As shown in the
figure, processing unit 504 may be implemented as one or more
independent processing units 506 and/or 508 with single or
multicore processors and processor caches included in each
processing unit. In other embodiments, processing unit 504 may also
be implemented as a quad-core processing unit or larger multicore
designs (e.g., hexa-core processors, octo-core processors, ten-core
processors, or greater).
Processing unit 504 may execute a variety of software processes
embodied in program code, and may maintain multiple concurrently
executing programs or processes. At any given time, some or all of
the program code to be executed can be resident in processor(s) 504
and/or in storage subsystem 510. In some embodiments, computer
system 500 may include one or more specialized processors, such as
digital signal processors (DSPs), outboard processors, graphics
processors, application-specific processors, and/or the like.
I/O subsystem 526 may include device controllers 528 for one or
more user interface input devices and/or user interface output
devices 530. User interface input and output devices 530 may be
integral with the computer system 500 (e.g., integrated audio/video
systems, and/or touchscreen displays), or may be separate
peripheral devices which are attachable/detachable from the
computer system 500. The I/O subsystem 526 may provide one or
several outputs to a user by converting one or several electrical
signals to the user in perceptible and/or interpretable form, and
may receive one or several inputs from the user by generating one
or several electrical signals based on one or several user-caused
interactions with the I/O subsystem such as the depressing of a key
or button, the moving of a mouse, the interaction with a
touchscreen or trackpad, the interaction of a sound wave with a
microphone, or the like.
Input devices 530 may include a keyboard, pointing devices such as
a mouse or trackball, a touchpad or touch screen incorporated into
a display, a scroll wheel, a click wheel, a dial, a button, a
switch, a keypad, audio input devices with voice command
recognition systems, microphones, and other types of input devices.
Input devices 530 may also include three dimensional (3D) mice,
joysticks or pointing sticks, game pads and graphic tablets, and
audio/visual devices such as speakers, digital cameras, digital
camcorders, portable media players, webcams, image scanners,
fingerprint scanners, barcode reader 3D scanners, 3D printers,
laser rangefinders, and eye gaze tracking devices. Additional input
devices 530 may include, for example, motion sensing and/or gesture
recognition devices that enable users to control and interact with
an input device through a natural user interface using gestures and
spoken commands, eye gesture recognition devices that detect eye
activity from users and transform the eye gestures as input into an
input device, voice recognition sensing devices that enable users
to interact with voice recognition systems through voice commands,
medical imaging input devices, MIDI keyboards, digital musical
instruments, and the like.
Output devices 530 may include one or more display subsystems,
indicator lights, or non-visual displays such as audio output
devices, etc. Display subsystems may include, for example, cathode
ray tube (CRT) displays, flat-panel devices, such as those using a
liquid crystal display (LCD) or plasma display, light-emitting
diode (LED) displays, projection devices, touch screens, and the
like. In general, use of the term "output device" is intended to
include all possible types of devices and mechanisms for outputting
information from computer system 500 to a user or other computer.
For example, output devices 530 may include, without limitation, a
variety of display devices that visually convey text, graphics and
audio/video information such as monitors, printers, speakers,
headphones, automotive navigation systems, plotters, voice output
devices, and modems.
Computer system 500 may comprise one or more storage subsystems
510, comprising hardware and software components used for storing
data and program instructions, such as system memory 518 and
computer-readable storage media 516. The system memory 518 and/or
computer-readable storage media 516 may store program instructions
that are loadable and executable on processing units 504, as well
as data generated during the execution of these programs.
Depending on the configuration and type of computer system 500,
system memory 318 may be stored in volatile memory (such as random
access memory (RAM) 512) and/or in non-volatile storage drives 514
(such as read-only memory (ROM), flash memory, etc.). The RAM 512
may contain data and/or program modules that are immediately
accessible to and/or presently being operated and executed by
processing units 504. In some implementations, system memory 518
may include multiple different types of memory, such as static
random access memory (SRAM) or dynamic random access memory (DRAM).
In some implementations, a basic input/output system (BIOS),
containing the basic routines that help to transfer information
between elements within computer system 500, such as during
start-up, may typically be stored in the non-volatile storage
drives 514. By way of example, and not limitation, system memory
518 may include application programs 520, such as client
applications, Web browsers, mid-tier applications, server
applications, etc., program data 522, and an operating system
524.
Storage subsystem 510 also may provide one or more tangible
computer-readable storage media 516 for storing the basic
programming and data constructs that provide the functionality of
some embodiments. Software (programs, code modules, instructions)
that, when executed by a processor, provide the functionality
described herein may be stored in storage subsystem 510. These
software modules or instructions may be executed by processing
units 504. Storage subsystem 510 may also provide a repository for
storing data used in accordance with the present invention.
Storage subsystem 300 may also include a computer-readable storage
media reader that can further be connected to computer-readable
storage media 516. Together and, optionally, in combination with
system memory 518, computer-readable storage media 516 may
comprehensively represent remote, local, fixed, and/or removable
storage devices plus storage media for temporarily and/or more
permanently containing, storing, transmitting, and retrieving
computer-readable information.
Computer-readable storage media 516 containing program code, or
portions of program code, may include any appropriate media known
or used in the art, including storage media and communication
media, such as but not limited to, volatile and non-volatile,
removable and non-removable media implemented in any method or
technology for storage and/or transmission of information. This can
include tangible computer-readable storage media such as RAM, ROM,
electronically erasable programmable ROM (EEPROM), flash memory or
other memory technology, CD-ROM, digital versatile disk (DVD), or
other optical storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or other tangible
computer readable media. This can also include nontangible
computer-readable media, such as data signals, data transmissions,
or any other medium which can be used to transmit the desired
information and which can be accessed by computer system 500.
By way of example, computer-readable storage media 516 may include
a hard disk drive that reads from or writes to non-removable,
nonvolatile magnetic media, a magnetic disk drive that reads from
or writes to a removable, nonvolatile magnetic disk, and an optical
disk drive that reads from or writes to a removable, nonvolatile
optical disk such as a CD ROM, DVD, and Blu-Ray.RTM. disk, or other
optical media. Computer-readable storage media 516 may include, but
is not limited to, Zip.RTM. drives, flash memory cards, universal
serial bus (USB) flash drives, secure digital (SD) cards, DVD
disks, digital video tape, and the like. Computer-readable storage
media 516 may also include, solid-state drives (SSD) based on
non-volatile memory such as flash-memory based SSDs, enterprise
flash drives, solid state ROM, and the like, SSDs based on volatile
memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based
SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a
combination of DRAM and flash memory based SSDs. The disk drives
and their associated computer-readable media may provide
non-volatile storage of computer-readable instructions, data
structures, program modules, and other data for computer system
500.
Communications subsystem 532 may provide a communication interface
from computer system 500 and external computing devices via one or
more communication networks, including local area networks (LANs),
wide area networks (WANs) (e.g., the Internet), and various
wireless telecommunications networks. As illustrated in FIG. 5, the
communications subsystem 532 may include, for example, one or more
network interface controllers (NICs) 534, such as Ethernet cards,
Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as
well as one or more wireless communications interfaces 536, such as
wireless network interface controllers (WNICs), wireless network
adapters, and the like. As illustrated in FIG. 5, the
communications subsystem 532 may include, for example, one or more
location determining features 538 such as one or several navigation
system features and/or receivers, and the like. Additionally and/or
alternatively, the communications subsystem 532 may include one or
more modems (telephone, satellite, cable, ISDN), synchronous or
asynchronous digital subscriber line (DSL) units, FireWire.RTM.
interfaces, USB.RTM. interfaces, and the like. Communications
subsystem 536 also may include radio frequency (RF) transceiver
components for accessing wireless voice and/or data networks (e.g.,
using cellular telephone technology, advanced data network
technology, such as 3G, 4G or EDGE (enhanced data rates for global
evolution), WiFi (IEEE 802.11 family standards, or other mobile
communication technologies, or any combination thereof), global
positioning system (GPS) receiver components, and/or other
components.
The various physical components of the communications subsystem 532
may be detachable components coupled to the computer system 500 via
a computer network, a FireWire.RTM. bus, or the like, and/or may be
physically integrated onto a motherboard of the computer system
500. Communications subsystem 532 also may be implemented in whole
or in part by software.
In some embodiments, communications subsystem 532 may also receive
input communication in the form of structured and/or unstructured
data feeds, event streams, event updates, and the like, on behalf
of one or more users who may use or access computer system 500. For
example, communications subsystem 532 may be configured to receive
data feeds in real-time from users of social networks and/or other
communication services, web feeds such as Rich Site Summary (RSS)
feeds, and/or real-time updates from one or more third party
information sources (e.g., data aggregators 311). Additionally,
communications subsystem 532 may be configured to receive data in
the form of continuous data streams, which may include event
streams of real-time events and/or event updates (e.g., sensor data
applications, financial tickers, network performance measuring
tools, clickstream analysis tools, automobile traffic monitoring,
etc.). Communications subsystem 532 may output such structured
and/or unstructured data feeds, event streams, event updates, and
the like to one or more data stores 104 that may be in
communication with one or more streaming data source computers
coupled to computer system 500.
Due to the ever-changing nature of computers and networks, the
description of computer system 500 depicted in the figure is
intended only as a specific example. Many other configurations
having more or fewer components than the system depicted in the
figure are possible. For example, customized hardware might also be
used and/or particular elements might be implemented in hardware,
firmware, software, or a combination. Further, connection to other
computing devices, such as network input/output devices, may be
employed. Based on the disclosure and teachings provided herein, a
person of ordinary skill in the art will appreciate other ways
and/or methods to implement the various embodiments.
With reference now to FIG. 6, a block diagram illustrating one
embodiment of the communication network is shown. Specifically,
FIG. 6 depicts one hardware configuration in which messages are
exchanged between a source hub 602 via the communication network
120 that can include one or several intermediate hubs 604. In some
embodiments, the source hub 602 can be any one or several
components of the content distribution network generating and
initiating the sending of a message, and the terminal hub 606 can
be any one or several components of the content distribution
network 100 receiving and not re-sending the message. In some
embodiments, for example, the source hub 602 can be one or several
of the user device 106, the supervisor device 110, and/or the
server 102, and the terminal hub 606 can likewise be one or several
of the user device 106, the supervisor device 110, and/or the
server 102. In some embodiments, the intermediate hubs 604 can
include any computing device that receives the message and resends
the message to a next node.
As seen in FIG. 6, in some embodiments, each of the hubs 602, 604,
606 can be communicatingly connected with the data store 104. In
such an embodiments, some or all of the hubs 602, 604, 606 can send
information to the data store 104 identifying a received message
and/or any sent or resent message. This information can, in some
embodiments, be used to determine the completeness of any sent
and/or received messages and/or to verify the accuracy and
completeness of any message received by the terminal hub 606.
In some embodiments, the communication network 120 can be formed by
the intermediate hubs 604. In some embodiments, the communication
network 120 can comprise a single intermediate hub 604, and in some
embodiments, the communication network 120 can comprise a plurality
of intermediate hubs. In one embodiment, for example, and as
depicted in FIG. 6, the communication network 120 includes a first
intermediate hub 604-A and a second intermediate hub 604-B.
With reference now to FIG. 7, a block diagram illustrating one
embodiment of user device 106 and supervisor device 110
communication is shown. In some embodiments, for example, a user
may have multiple devices that can connect with the content
distribution network 100 to send or receive information. In some
embodiments, for example, a user may have a personal device such as
a mobile device, a Smartphone, a tablet, a Smartwatch, a laptop, a
PC, or the like. In some embodiments, the other device can be any
computing device in addition to the personal device. This other
device can include, for example, a laptop, a PC, a Smartphone, a
tablet, a Smartwatch, or the like. In some embodiments, the other
device differs from the personal device in that the personal device
is registered as such within the content distribution network 100
and the other device is not registered as a personal device within
the content distribution network 100.
Specifically with respect to FIG. 7, the user device 106 can
include a personal user device 106-A and one or several other user
devices 106-B. In some embodiments, one or both of the personal
user device 106-A and the one or several other user devices 106-B
can be communicatingly connected to the content management server
102 and/or to the navigation system 122. Similarly, the supervisor
device 110 can include a personal supervisor device 110-A and one
or several other supervisor devices 110-B. In some embodiments, one
or both of the personal supervisor device 110-A and the one or
several other supervisor devices 110-B can be communicatingly
connected to the content management server 102 and/or to the
navigation system 122.
In some embodiments, the content distribution network can send one
or more alerts to one or more user devices 106 and/or one or more
supervisor devices 110 via, for example, the communication network
120. In some embodiments, the receipt of the alert can result in
the launching of an application within the receiving device, and in
some embodiments, the alert can include a link that, when selected,
launches the application or navigates a web-browser of the device
of the selector of the link to a page or portal associated with the
alert. In some embodiments, this launched application can display
one or several items or pieces of data contained in the alert.
In some embodiments, for example, the providing of this alert can
include the identification of one or several user devices 106
and/or student-user accounts associated with the student-user
and/or one or several supervisor devices 110 and/or supervisor-user
accounts associated with the supervisor-user. After these one or
several devices 106, 110 and/or accounts have been identified, the
providing of this alert can include determining an active device of
the devices 106, 110 based on determining which of the devices 106,
110 and/or accounts are actively being used, and then providing the
alert to that active device.
Specifically, if the user is actively using one of the devices 106,
110 such as the other user device 106-B and the other supervisor
device 110-B, and/or accounts, the alert can be provided to the
user via that other device 106-B, 110-B and/or account that is
actively being used. If the user is not actively using an other
device 106-B, 110-B and/or account, a personal device 106-A, 110-A
device, such as a smart phone or tablet, can be identified and the
alert can be provided to this personal device 106-A, 110-A. In some
embodiments, the alert can include code to direct the default
device to provide an indicator of the received alert such as, for
example, an aural, tactile, or visual indicator of receipt of the
alert.
In some embodiments, the recipient device 106, 110 of the alert can
provide an indication of receipt of the alert. In some embodiments,
the presentation of the alert can include the control of the I/O
subsystem 526 to, for example, provide an aural, tactile, and/or
visual indicator of the alert and/or of the receipt of the alert.
In some embodiments, this can include controlling a screen of the
supervisor device 110 to display the alert, data contained in alert
and/or an indicator of the alert.
With reference now to FIG. 8, a schematic illustration of one
embodiment of an application stack, and particularly of a stack 650
is shown. In some embodiments, the content distribution network 100
can comprise a portion of the stack 650 that can include an
infrastructure layer 652, a platform layer 654, an applications
layer 656, and a products layer 658. In some embodiments, the stack
650 can comprise some or all of the layers, hardware, and/or
software to provide one or several desired functionalities and/or
productions.
As depicted in FIG. 8, the infrastructure layer 652 can include one
or several servers, communication networks, data stores, privacy
servers, and the like. In some embodiments, the infrastructure
layer can further include one or several user devices 106 and/or
supervisor devices 110 connected as part of the content
distribution network.
The platform layer can include one or several platform software
programs, modules, and/or capabilities. These can include, for
example, identification services, security services, and/or
adaptive platform services 660. In some embodiments, the
identification services can, for example, identify one or several
users, components of the content distribution network 100, or the
like. The security services can monitor the content distribution
network for one or several security threats, breaches, viruses,
malware, or the like. The adaptive platform services 660 can
receive information from one or several components of the content
distribution network 100 and can provide predictions, models,
recommendations, or the like based on that received information.
The functionality of the adaptive platform services 660 will be
discussed in greater detail in FIGS. 9A-9C, below.
The applications layer 656 can include software or software modules
upon or in which one or several product softwares or product
software modules can operate. In some embodiments, the applications
layer 656 can include, for example, a management system, record
system, or the like. In some embodiments, the management system can
include, for example, a Learning Management System (LMS), a Content
Management System (CMS), or the like. The management system can be
configured to control the delivery of one or several resources to a
user and/or to receive one or several responses from the user. In
some embodiments, the records system can include, for example, a
virtual gradebook, a virtual counselor, or the like.
The products layer can include one or several software products
and/or software module products. These software products and/or
software module products can provide one or several services and/or
functionalities to one or several users of the software products
and/or software module products.
With reference now to FIG. 9A-9C, schematic illustrations of
embodiments of communication and processing flow of modules within
the content distribution network 100 are shown. In some
embodiments, the communication and processing can be performed in
portions of the platform layer 654 and/or applications layer 656.
FIG. 9A depicts a first embodiment of such communications or
processing that can be in the platform layer 654 and/or
applications layer 656 via the message channel 412.
The platform layer 654 and/or applications layer 656 can include a
plurality of modules that can be embodied in software or hardware.
In some embodiments, some or all of the modules can be embodied in
hardware and/or software at a single location, and in some
embodiments, some or all of these modules can be embodied in
hardware and/or software at multiple locations. These modules can
perform one or several processes including, for example, a
presentation process 670, a response process 676, a summary model
process 680, and a packet selection process 684.
The presentation process 670 can, in some embodiments, include one
or several methods and/or steps to deliver content to one or
several user devices 106 and/or supervisor devices 110. The
presentation process 670 can be performed by a presenter module 672
and a view module 674. The presenter module 672 can be a hardware
or software module of the content distribution network 100, and
specifically of the server 102. In some embodiments, the presenter
module 672 can include one or several portions, features, and/or
functionalities that are located on the server 102 and/or one or
several portions, features, and/or functionalities that are located
on the user device 106. In some embodiments, the presenter module
672 can be embodied in the presentation system 408.
The presenter module 672 can control the providing of content to
one or several user devices 106 and/or supervisor devices 110.
Specifically, the presenter module 672 can control the generation
of one or several messages to provide content to one or several
desired user devices 106 and/or supervisor devices 110. The
presenter module 672 can further control the providing of these one
or several messages to the desired one or several desired user
devices 106 and/or supervisor devices 110. Thus, in some
embodiments, the presenter module 672 can control one or several
features of the communications subsystem 532 to generate and send
one or several electrical signals comprising content to one or
several user devices 106 and/or supervisor devices 110.
In some embodiments, the presenter module 672 can control and/or
manage a portion of the presentation functions of the presentation
process 670, and can specifically manage an "outer loop" of
presentation functions. As used herein, the outer loop refers to
tasks relating to the tracking of a user's progress through all or
a portion of a group of data packets. In some embodiments, this can
include the identification of one or several completed data packets
or nodes and/or the non-adaptive selection of one or several next
data packets or nodes according to, for example, one or several
fixed rules. Such non-adaptive selection does not rely on the use
of predictive models, but rather on rules identifying next data
packets based on data relating to the completion of one or several
previously completed data packets or assessments and/or whether one
or several previously completed data packets were successfully
completed.
In some embodiments, and due to the management of the outer loop of
presentation functions including the non-adaptive selection of one
or several next data packets, nodes, or tasks by the presenter
module, the presenter module can function as a recommendation
engine referred to herein as a first recommendation engine or a
rules-based recommendation engine. In some embodiments, the first
recommendation engine can be configured to select a next node for a
user based on one or all of: the user's current location in the
content network; potential next nodes; the user's history including
the user's previous responses; and one or several guard conditions
associated with the potential next nodes. In some embodiments, a
guard condition defines one or several prerequisites for entry
into, or exit from, a node.
In some embodiments, the presenter module 672 can include a portion
located on the server 102 and/or a portion located on the user
device 106. In some embodiments, the portion of the presenter
module 672 located on the server 102 can receive data packet
information and provide a subset of the received data packet
information to the portion of the presenter module 672 located on
the user device 106. In some embodiments, this segregation of
functions and/or capabilities can prevent solution data from being
located on the user device 106 and from being potentially
accessible by the user of the user device 106.
In some embodiments, the portion of the presenter module 672
located on the user device 106 can be further configured to receive
the subset of the data packet information from the portion of the
presenter module 672 located on the server 102 and provide that
subset of the data packet information to the view module 674. In
some embodiments, the portion of the presenter module 672 located
on the user device 106 can be further configured to receive a
content request from the view module 674 and to provide that
content request to the portion of the presenter module 674 located
on the server 102.
The view module 674 can be a hardware or software module of some or
all of the user devices 106 and/or supervisor devices 110 of the
content distribution network 100. The view module 674 can receive
one or several electrical signals and/or communications from the
presenter module 672 and can provide the content received in those
one or several electrical signals and/or communications to the user
of the user device 106 and/or supervisor device 110 via, for
example, the I/O subsystem 526.
In some embodiments, the view module 674 can control and/or monitor
an "inner loop" of presentation functions. As used herein, the
inner loop refers to tasks relating to the tracking and/or
management of a user's progress through a data packet. This can
specifically relate to the tracking and/or management of a user's
progression through one or several pieces of content, questions,
assessments, and/or the like of a data packet. In some embodiments,
this can further include the selection of one or several next
pieces of content, next questions, next assessments, and/or the
like of the data packet for presentation and/or providing to the
user of the user device 106.
In some embodiments, one or both of the presenter module 672 and
the view module 674 can comprise one or several presentation
engines. In some embodiments, these one or several presentation
engines can comprise different capabilities and/or functions. In
some embodiments, one of the presentation engines can be configured
to track the progress of a user through a single data packet, task,
content item, or the like, and in some embodiments, one of the
presentation engines can track the progress of a user through a
series of data packets, tasks, content items, or the like.
The response process 676 can comprise one or several methods and/or
steps to evaluate a response. In some embodiments, this can
include, for example, determining whether the response comprises a
desired response and/or an undesired response. In some embodiments,
the response process 676 can include one or several methods and/or
steps to determine the correctness and/or incorrectness of one or
several received responses. In some embodiments, this can include,
for example, determining the correctness and/or incorrectness of a
multiple choice response, a true/false response, a short answer
response, an essay response, or the like. In some embodiments, the
response processor can employ, for example, natural language
processing, semantic analysis, or the like in determining the
correctness or incorrectness of the received responses.
In some embodiments, the response process 676 can be performed by a
response processor 678. The response processor 678 can be a
hardware or software module of the content distribution network
100, and specifically of the server 102. In some embodiments, the
response processor 678 can be embodied in the response system 406.
In some embodiments, the response processor 678 can be
communicatingly connected to one or more of the modules of the
presentation process 760 such as, for example, the presenter module
672 and/or the view module 674. In some embodiments, the response
processor 678 can be communicatingly connected with, for example,
the message channel 412 and/or other components and/or modules of
the content distribution network 100.
The summary model process 680 can comprise one or several methods
and/or steps to generate and/or update one or several models. In
some embodiments, this can include, for example, implementing
information received either directly or indirectly from the
response processor 678 to update one or several models. In some
embodiments, the summary model process 680 can include the update
of a model relating to one or several user attributes such as, for
example, a user skill model, a user knowledge model, a learning
style model, or the like. In some embodiments, the summary model
process 680 can include the update of a model relating to one or
several content attributes including attributes relating to a
single content item and/or data packet and/or attributes relating
to a plurality of content items and/or data packets. In some
embodiments, these models can relate to an attribute of the one or
several data packets such as, for example, difficulty,
discrimination, required time, or the like.
In some embodiments, the summary model process 680 can be performed
by the model engine 682. In some embodiments, the model engine 682
can be a hardware or software module of the content distribution
network 100, and specifically of the server 102. In some
embodiments, the model engine 682 can be embodied in the summary
model system 404.
In some embodiments, the model engine 682 can be communicatingly
connected to one or more of the modules of the presentation process
760 such as, for example, the presenter module 672 and/or the view
module 674, can be connected to the response processor 678 and/or
the recommendation. In some embodiment, the model engine 682 can be
communicatingly connected to the message channel 412 and/or other
components and/or modules of the content distribution network
100.
The packet selection process 684 can comprise one or several steps
and/or methods to identify and/or select a data packet for
presentation to a user. In some embodiments, this data packet can
comprise a plurality of data packets. In some embodiments, this
data packet can be selected according to one or several models
updated as part of the summary model process 680. In some
embodiments, this data packet can be selected according to one or
several rules, probabilities, models, or the like. In some
embodiments, the one or several data packets can be selected by the
combination of a plurality of models updated in the summary model
process 680 by the model engine 682. In some embodiments, these one
or several data packets can be selected by a recommendation engine
686. The recommendation engine 686 can be a hardware or software
module of the content distribution network 100, and specifically of
the server 102. In some embodiments, the recommendation engine 686
can be embodied in the packet selection system 402. In some
embodiments, the recommendation engine 686 can be communicatingly
connected to one or more of the modules of the presentation process
670, the response process 676, and/or the summary model process 680
either directly and/or indirectly via, for example, the message
channel.
In some embodiments, and as depicted in FIG. 9A, a presenter module
672 can receive a data packet for presentation to a user device
106. This data packet can be received, either directly or
indirectly from a recommendation engine 686. In some embodiments,
for example, the presenter module 672 can receive a data packet for
providing to a user device 106 from the recommendation engine 686,
and in some embodiments, the presenter module 672 can receive an
identifier of a data packet for providing to a user device 106 via
a view module 674. This can be received from the recommendation
engine 686 via a message channel 412. Specifically, in some
embodiments, the recommendation engine 686 can provide data to the
message channel 412 indicating the identification and/or selection
of a data packet for providing to a user via a user device 106. In
some embodiments, this data indicating the identification and/or
selection of the data packet can identify the data packet and/or
can identify the intended recipient of the data packet.
The message channel 412 can output this received data in the form
of a data stream 690 which can be received by, for example, the
presenter module 672, the model engine 682, and/or the
recommendation engine 686. In some embodiments, some or all of: the
presenter module 672, the model engine 682, and/or the
recommendation engine 686 can be configured to parse and/or filter
the data stream 690 to identify data and/or events relevant to
their operation. Thus, for example, the presenter module 672 can be
configured to parse the data stream for information and/or events
relevant to the operation of the presenter module 672.
In some embodiments, the presenter module 672 can extract the data
packet from the data stream 690 and/or extract data identifying the
data packet and/or indicating the selecting of a data packet from
the data stream. In the event that data identifying the data packet
is extracted from the data stream 690, the presenter module 672 can
request and receive the data packet from the database server 104,
and specifically from the content library database 303. In
embodiments in which data indicating the selection of a data packet
is extracted from the data stream 690, the presenter module 672 can
request and receive identification of the data packet from the
recommendation engine 686 and then request and receive the data
packet from the database server 104, and specifically from the
content library database 303, and in some embodiments in which data
indicating the selection of a data packet is extracted from the
data stream 690, the presenter module 672 can request and receive
the data packet from the recommendation engine 686.
The presenter module can then provide the data packet and/or
portions of the data packet to the view module 674. In some
embodiments, for example, the presenter module 672 can retrieve one
or several rules and/or conditions that can be, for example,
associated with the data packet and/or stored in the database
server 104. In some embodiments, these rules and/or conditions can
identify portions of a data packet for providing to the view module
674 and/or portions of a data packet to not provide to the view
module 674. In some embodiments, for example, sensitive portions of
a data packet, such as, for example, solution information to any
questions associated with a data packet, is not provided to the
view module 674 to prevent the possibility of undesired access to
those sensitive portions of the data packet. Thus, in some
embodiments, the one or several rules and/or conditions can
identify portions of the data packet for providing to the view
module 674 and/or portions of the data packet for not providing to
the view module.
In some embodiments, the presenter module 672 can, according to the
one or more rules and/or conditions, generate and transmit an
electronic message containing all or portions of the data packet to
the view module 674. The view module 674 can receive these all or
portions of the data packet and can provide all or portions of this
information to the user of the user device 106 associated with the
view module 674 via, for example, the I/O subsystem 526. In some
embodiments, as part of the providing of all or portions of the
data packet to the user of the view module 674, one or several user
responses can be received by the view module 674. In some
embodiments, these one or several user responses can be received
via the I/O subsystem 526 of the user device 106.
After one or several user responses have been received, the view
module 674 can provide the one or several user responses to the
response processor 678. In some embodiments, these one or several
responses can be directly provided to the response processor 678,
and in some embodiments, these one or several responses can be
provided indirectly to the response processor 678 via the message
channel 412.
After the response processor 678 receives the one or several
responses, the response processor 678 can determine whether the
responses are desired responses and/or the degree to which the
received responses are desired responses. In some embodiments, the
response processor can make this determination via, for example,
use of one or several techniques, including, for example, natural
language processing (NLP), semantic analysis, or the like.
In some embodiments, the response processor can determine whether a
response is a desired response and/or the degree to which a
response is a desired response with comparative data which can be
associated with the data packet. In some embodiments, this
comparative data can comprise, for example, an indication of a
desired response and/or an indication of one or several undesired
responses, a response key, a response rubric comprising one
criterion or several criteria for determining the degree to which a
response is a desired response, or the like. In some embodiments,
the comparative data can be received as a portion of and/or
associated with a data packet. In some embodiments, the comparative
data can be received by the response processor 678 from the
presenter module 672 and/or from the message channel 412. In some
embodiments, the response data received from the view module 674
can comprise data identifying the user and/or the data packet or
portion of the data packet with which the response is associated.
In some embodiments in which the response processor 678 merely
receives data identifying the data packet and/or portion of the
data packet associated with the one or several responses, the
response processor 678 can request and/or receive comparative data
from the database server 104, and specifically from the content
library database 303 of the database server 104.
After the comparative data has been received, the response
processor 678 determines whether the one or several responses
comprise desired responses and/or the degree to which the one or
several responses comprise desired responses. The response
processor can then provide the data characterizing whether the one
or several responses comprise desired response and/or the degree to
which the one or several responses comprise desired responses to
the message channel 412. The message channel can, as discussed
above, include the output of the response processor 678 in the data
stream 690 which can be constantly output by the message channel
412.
In some embodiments, the model engine 682 can subscribe to the data
stream 690 of the message channel 412 and can thus receive the data
stream 690 of the message channel 412 as indicated in FIG. 9A. The
model engine 682 can monitor the data stream 690 to identify data
and/or events relevant to the operation of the model engine. In
some embodiments, the model engine 682 can monitor the data stream
690 to identify data and/or events relevant to the determination of
whether a response is a desired response and/or the degree to which
a response is a desired response.
When a relevant event and/or relevant data are identified by the
model engine, the model engine 682 can take the identified relevant
event and/or relevant data and modify one or several models. In
some embodiments, this can include updating and/or modifying one or
several models relevant to the user who provided the responses,
updating and/or modifying one or several models relevant to the
data packet associated with the responses, and/or the like. In some
embodiments, these models can be retrieved from the database server
104, and, in some embodiments, can be retrieved from the model data
source 309 of the database server 104.
After the models have been updated, the updated models can be
stored in the database server 104. In some embodiments, the model
engine 682 can send data indicative of the event of the completion
of the model update to the message channel 412. The message channel
412 can incorporate this information into the data stream 690 which
can be received by the recommendation engine 686. The
recommendation engine 686 can monitor the data stream 690 to
identify data and/or events relevant to the operation of the
recommendation engine 686. In some embodiments, the recommendation
engine 686 can monitor the data stream 690 to identify data and/or
events relevant to the updating of one or several models by the
model engine 682.
When the recommendation engine 686 identifies information in the
data stream 690 indicating the completion of the summary model
process 680 for models relevant to the user providing the response
and/or for models relevant to the data packet provided to the user,
the recommendation engine 686 can identify and/or select a next
data packet for providing to the user and/or to the presentation
process 470. In some embodiments, this selection of the next data
packet can be performed according to one or several rules and/or
conditions. After the next data packet has been selected, the
recommendation engine 686 can provide information to the model
engine 682 identifying the next selected data packet and/or to the
message channel 412 indicating the event of the selection of the
next content item. After the message channel 412 receives
information identifying the selection of the next content item
and/or receives the next content item, the message channel 412 can
include this information in the data stream 690 and the process
discussed with respect to FIG. 9A can be repeated.
With reference now to FIG. 9B, a schematic illustration of a second
embodiment of communication or processing that can be in the
platform layer 654 and/or applications layer 656 via the message
channel 412 is shown. In the embodiment depicted in FIG. 9B, the
data packet provided to the presenter module 672 and then to the
view module 674 does not include a prompt for a user response
and/or does not result in the receipt of a user response. As no
response is received, when the data packet is completed, nothing is
provided to the response processor 678, but rather data indicating
the completion of the data packet is provided from one of the view
module 674 and/or the presenter module 672 to the message channel
412. The data is then included in the data stream 690 and is
received by the model engine 682 which uses the data to update one
or several models. After the model engine 682 has updated the one
or several models, the model engine 682 provides data indicating
the completion of the model updates to the message channel 412. The
message channel 412 then includes the data indicating the
completion of the model updates in the data stream 690 and the
recommendation engine 686, which can subscribe to the data stream
690, can extract the data indicating the completion of the model
updates from the data stream 690. The recommendation engine 686 can
then identify a next one or several data packets for providing to
the presenter module 672, and the recommendation engine 686 can
then, either directly or indirectly, provide the next one or
several data packets to the presenter module 672.
In some embodiments, of the communication as shown in FIGS. 9A and
9B, all communications between any of the presenter module 672, the
response processor 678, the model engine 682, and the
recommendation engine 686 can pass through the message channel 412.
Alternatively, in some embodiments, some of the communications
between any of the presenter module 672, the response processor
678, the model engine 682, and the recommendation engine 686 can
pass through the message channel and others of the communications
between any of the presenter module 672, the response processor
678, the model engine 682, and the recommendation engine 686 can be
direct.
With reference now to FIG. 9C, a schematic illustration of an
embodiment of dual communication, or hybrid communication, in the
platform layer 654 and/or application layer 656 is shown.
Specifically, in this embodiment, some communication is synchronous
with the completion of one or several tasks and some communication
is asynchronous. In the embodiment depicted in FIG. 9C, the
presenter module 972 communicates synchronously with the model
engine 682 via a direct communication 692 and communicates
asynchronously with the model engine 682 via the message channel
412.
In some embodiments, and as depicted in FIG. 9C, the synchronous
communication and/or the operation of the presenter module 672, the
response processor 678, the model engine 682, and the
recommendation engine 686 can be directed and/or controlled by a
controller. In some embodiments, this controller can be part of the
server 102 and/or located in any one or more of the presenter
module 672, the response processor 678, the model engine 682, and
the recommendation engine 686. In some embodiments, this controller
can be located in the presenter module 672, which presenter module
can control communications with and between itself and the response
processor 678, the model engine 682, and the recommendation engine
686, and the presenter module can thus control the functioning of
the response processor 678, the model engine 682, and the
recommendation engine 686.
Specifically, and with reference to FIG. 9C, the presenter module
672 can receive and/or select a data packet for presentation to the
user device 106 via the view module 674. In some embodiments, the
presenter module 672 can identify all or portions of the data
packet that can be provided to the view module 674 and portions of
the data packet for retaining from the view module 674. In some
embodiments, the presenter module can provide all or portions of
the data packet to the view module 674. In some embodiments, and in
response to the receipt of all or portions of the data packet, the
view module 674 can provide a confirmation of receipt of the all or
portions of the data packet and can provide those all or portions
of the data packet to the user via the user device 106. In some
embodiments, the view module 674 can provide those all or portions
of the data packet to the user device 106 while controlling the
inner loop of the presentation of the data packet to the user via
the user device 106.
After those all or portions of the data packet have been provided
to the user device 106, a response indicative of the completion of
one or several tasks associated with the data packet can be
received by the view module 674 from the user device 106, and
specifically from the I/O subsystem 526 of the user device 106. In
response to this receive, the view module 674 can provide an
indication of this completion status to the presenter module 672
and/or can provide the response to the response processor 678.
After the response has been received by the response processor 678,
the response processor 678 can determine whether the received
response is a desired response. In some embodiments, this can
include, for example, determining whether the response comprises a
correct answer and/or the degree to which the response comprises a
correct answer.
After the response processor has determined whether the received
response is a desired response, the response processor 678 can
provide an indicator of the result of the determination of whether
the received response is a desired response to the presenter module
672. In response to the receipt of the indicator of whether the
result of the determination of whether the received response is a
desired response, the presenter module 672 can synchronously
communicate with the model engine 682 via a direct communication
692 and can asynchronously communicate with model engine 682 via
the message channel 412. In some embodiments, the synchronous
communication can advantageously include two-way communication
between the model engine 682 and the presenter module 672 such that
the model engine 682 can provide an indication to the presenter
module 672 when model updating is completed by the model
engine.
After the model engine 682 has received one or both of the
synchronous and asynchronous communications, the model engine 682
can update one or several models relating to, for example, the
user, the data packet, or the like. After the model engine 682 has
completed the updating of the one or several models, the model
engine 682 can send a communication to the presenter module 672
indicating the completion of the updated one or several
modules.
After the presenter module 672 receives the communication
indicating the completion of the updating of the one or several
models, the presenter module 672 can send a communication to the
recommendation engine 686 requesting identification of a next data
packet. As discussed above, the recommendation engine 686 can then
retrieve the updated model and retrieve the user information. With
the updated models and the user information, the recommendation
engine can identify a next data packet for providing to the user,
and can provide the data packet to the presenter module 672. In
some embodiments, the recommendation engine 686 can further provide
an indication of the next data packet to the model engine 682,
which can use this information relating to the next data packet to
update one or several models, either immediately, or after
receiving a communication from the presenter module 672 subsequent
to the determination of whether a received response for that data
packet is a desired response.
With reference now to FIG. 9D, a schematic illustration of one
embodiment of the presentation process 670 is shown. Specifically,
FIG. 9D depicts multiple portions of the presenter module 672,
namely, the external portion 673 and the internal portion 675. In
some embodiment, the external portion 673 of the presenter module
672 can be located in the server, and in some embodiments, the
internal portion 675 of the presenter module 672 can be located in
the user device 106. In some embodiments, the external portion 673
of the presenter module can be configured to communicate and/or
exchange data with the internal portion 675 of the presenter module
672 as discussed herein. In some embodiments, for example, the
external portion 673 of the presenter module 672 can receive a data
packet and can parse the data packet into portions for providing to
the internal portion 675 of the presenter module 672 and portions
for not providing to the internal portion 675 of the presenter
module 672. In some embodiments, the external portion 673 of the
presenter module 672 can receive a request for additional data
and/or an additional data packet from the internal portion 675 of
the presenter module 672. In such an embodiments, the external
portion 673 of the presenter module 672 can identify and retrieve
the requested data and/or the additional data packet from, for
example, the database server 104 and more specifically from the
content library database 104.
With reference now to FIG. 10A, a flowchart illustrating one
embodiment of a process 440 for data management is shown. In some
embodiments, the process 440 can be performed by the content
management server 102, and more specifically by the presentation
system 408 and/or by the presentation module or presentation
engine. In some embodiments, the process 440 can be performed as
part of the presentation process 670.
The process 440 begins at block 442, wherein a data packet is
identified. In some embodiments, the data packet can be a data
packet for providing to a student-user. In some embodiments, the
data packet can be identified based on a communication received
either directly or indirectly from the recommendation engine
686.
After the data packet has been identified, the process 440 proceeds
to block 444, wherein the data packet is requested. In some
embodiments, this can include the requesting of information
relating to the data packet such as the data forming the data
packet. In some embodiments, this information can be requested
from, for example, the content library database 303. After the data
packet has been requested, the process 440 proceeds to block 446,
wherein the data packet is received. In some embodiments, the data
packet can be received by the presentation system 408 from, for
example, the content library database 303.
After the data packet has been received, the process 440 proceeds
to block 448, wherein one or several data components are
identified. In some embodiments, for example, the data packet can
include one or several data components which can, for example,
contain different data. In some embodiments, one of these data
components, referred to herein as a presentation component, can
include content for providing to the student user, which content
can include one or several requests and/or questions and/or the
like. In some embodiments, one of these data components, referred
to herein as a response component, can include data used in
evaluating one or several responses received from the user device
106 in response to the data packet, and specifically in response to
the presentation component and/or the one or several requests
and/or questions of the presentation component. Thus, in some
embodiments, the response component of the data packet can be used
to ascertain whether the user has provided a desired response or an
undesired response.
After the data components have been identified, the process 440
proceeds to block 450, wherein a delivery data packet is
identified. In some embodiments, the delivery data packet can
include the one or several data components of the data packets for
delivery to a user such as the student-user via the user device
106. In some embodiments, the delivery packet can include the
presentation component, and in some embodiments, the delivery
packet can exclude the response packet. After the delivery data
packet has been generated, the process 440 proceeds to block 452,
wherein the delivery data packet is provided to the user device 106
and more specifically to the view module 674. In some embodiments,
this can include providing the delivery data packet to the user
device 106 via, for example, the communication network 120.
After the delivery data packet has been provided to the user device
106, the process 440 proceeds to block 454, wherein the data packet
and/or one or several components thereof are sent to and/or
provided to the response processor 678. In some embodiments, this
sending of the data packet and/or one or several components thereof
to the response processor can include receiving a response from the
student-user, and sending the response to the student-user to the
response processor simultaneous with the sending of the data packet
and/or one or several components thereof to the response processor.
In some embodiments, for example, this can include providing the
response component to the response processor. In some embodiments,
the response component can be provided to the response processor
from the presentation system 408.
With reference now to FIG. 10B, a flowchart illustrating one
embodiment of a process 460 for evaluating a response is shown. In
some embodiments, the process can be performed as a part of the
response process 676 and can be performed by, for example, the
response system 406 and/or by the response processor 678. In some
embodiments, the process 460 can be performed by the response
system 406 in response to the receipt of a response, either
directly or indirectly, from the user device 106 or from the view
module 674.
The process 460 begins at block 462, wherein a response is received
from, for example, the user device 106 via, for example, the
communication network 120. After the response has been received,
the process 460 proceeds to block 464, wherein the data packet
associated with the response is received. In some embodiments, this
can include receiving all or one or several components of the data
packet such as, for example, the response component of the data
packet. In some embodiments, the data packet can be received by the
response processor from the presentation engine.
After the data packet has been received, the process 460 proceeds
to block 466, wherein the response type is identified. In some
embodiments, this identification can be performed based on data,
such as metadata associated with the response. In other
embodiments, this identification can be performed based on data
packet information such as the response component.
In some embodiments, the response type can identify one or several
attributes of the one or several requests and/or questions of the
data packet such as, for example, the request and/or question type.
In some embodiments, this can include identifying some or all of
the one or several requests and/or questions as true/false,
multiple choice, short answer, essay, or the like.
After the response type has been identified, the process 460
proceeds to block 468, wherein the data packet and the response are
compared to determine whether the response comprises a desired
response and/or an undesired response. In some embodiments, this
can include comparing the received response and the data packet to
determine if the received response matches all or portions of the
response component of the data packet, to determine the degree to
which the received response matches all or portions of the response
component, to determine the degree to which the receive response
embodies one or several qualities identified in the response
component of the data packet, or the like. In some embodiments,
this can include classifying the response according to one or
several rules. In some embodiments, these rules can be used to
classify the response as either desired or undesired. In some
embodiments, these rules can be used to identify one or several
errors and/or misconceptions evidenced in the response. In some
embodiments, this can include, for example: use of natural language
processing software and/or algorithms; use of one or several
digital thesauruses; use of lemmatization software, dictionaries,
and/or algorithms; or the like.
After the data packet and the response have been compared, the
process 460 proceeds to block 470 wherein response desirability is
determined. In some embodiments, this can include, based on the
result of the comparison of the data packet and the response,
whether the response is a desired response or is an undesired
response. In some embodiments, this can further include quantifying
the degree to which the response is a desired response. This
determination can include, for example, determining if the response
is a correct response, an incorrect response, a partially correct
response, or the like. In some embodiments, the determination of
response desirability can include the generation of a value
characterizing the response desirability and the storing of this
value in one of the databases 104 such as, for example, the user
profile database 301. After the response desirability has been
determined, the process 460 proceeds to block 472, wherein an
assessment value is generated. In some embodiments, the assessment
value can be an aggregate value characterizing response
desirability for one or more a plurality of responses. This
assessment value can be stored in one of the databases 104 such as
the user profile database 301.
With reference to the FIG. 11, a flowchart illustrating one
embodiment of a process 1100 for automatic difficulty determination
of the data packet and placement of that data packet in the content
network is shown. The process 1100 can be performed by all or
components of the content distribution network 100 including by,
for example, one or several servers 102. In some embodiments, these
one or several servers 102 can comprise one or several remote
resources such as can occur via cloud computing or distributed
processing. The process 1100 begins at block 1102 when a data
packet is received and/or identified. In some embodiments, the data
packet can be a data packet for which responses have not been
received. In some embodiments, this data packet does not include
data metadata identifying one or several attributes of the data
packet such as, for example, data packet difficulty level,
discrimination level, or the like. In some embodiments, the data
packet can be received by the server 102 from another component of
the content distribution network 100 such as, for example, the
supervisor device 110 or content server 112. In some embodiments,
the data packet can be identified by the server 102 from one or
several data packets stored in the database server 104 such as in,
for example, the content library database 303 of the database
server 104.
After the data packet has been received and/or identified, the
process 1100 proceeds to block 1104 wherein data packet
information, also referred to herein as data packet metadata, is
retrieved and/or received. In some embodiments, the data packet
metadata can identify one or several attributes of the data packet
such as, for example, the difficulty level of all or portions of
the data packet, the discrimination level of all or portions of the
data packet, or the like. In some embodiments, the data packet
metadata can be received with the data packet in block 1102, and in
some embodiments, the data packet metadata can be retrieved from
the database server 104 and specifically from, for example, the
content library database 303 of the database server 104.
After the data packet information has been retrieved, the process
1100 proceeds to block 1106 wherein it is determined if the data
packet metadata includes data identifying a difficulty level and/or
discrimination level of the data packet. In some embodiments, this
can include analyzing the data packet metadata in determining the
presence or absence of portions the data packet metadata
identifying a difficulty level and/or discrimination level of the
data packet. In some embodiments, this determination can be made by
the server 102. If it is determined that the data packet metadata
contains data packet difficulty information and/or data packet
discrimination information, then the process 1100 proceeds to block
1107 and continues to block 1206 of FIG. 12 with the data packet
received in block 1102 and continues as described with respect to
FIG. 12 below.
The process 1100 continues to decision state 1108 wherein it is
determined if any additional data packets have been received or
retrieved. If no additional data packets have been received or
retrieved, then the process proceeds to block 1109 and waits until
a new data packet has been received. After the new data packet has
been received, the process 1100 returns to block 1104 and
procedures outlined herein. Returning again to decision state 1108,
if it is determined that there is an additional data packet, then
the process 1100 returns to block 1104 and procedures outlined
herein.
Returning again to decision state 1106, if it is determined the
difficulty level of the data packet received in block 1102 has not
been determined, then the process 1100 proceeds to block 1110
wherein the data packet content network of the received data packet
is identified. In some embodiments, for example, the data packet
content network can be identified based on information contained in
the data packet metadata and information relating to the content
network which information relating to the content network is
referred to herein as content network metadata. In some
embodiments, the information contained in the data packet metadata
can identify a subject of the data packet and/or a subject to which
the data packet belongs. Similarly, in some embodiments, the
content network metadata can identify a subject of the content
network and/or tasks, skills, objectives, topics, or the like
belonging to the content network. In some embodiments, the
information contained in the data packet metadata and in the
content network metadata can be retrieved and/or extracted. The
information contained in the data packet metadata can then be
matched to the information contained in content network metadata to
identify the content network to which the data packet belongs. This
content network to which the data packet belongs is also referred
to herein as the data packet content network. In some embodiments,
the identification of the data packet content network can be
performed by the server 102 and/or any of the modules are engines
of the server 102 or other portion of the content distribution
network 100.
After the data packet content network has been identified, the
process 1100 proceeds to block 1112 wherein a position in the
content network is identified and/or determined for insertion of
the data packet received in block 1102. In some embodiments, the
position of the content network can be identified based on the data
packet metadata and/or the content network metadata. In some
embodiments, the position of the content network can be identified
and/or selected such that the data packet is placed in the content
network with other data packets having similar subjects, topics,
skill levels, or the like. This identification can be performed by
the server 102 or any other component of the content distribution
network 100.
After the position of the content network has been identified, the
process 1100 proceeds to block 1114 wherein similarly positioned
data packets are identified. In some embodiments, this
identification can be performed by retrieving data packets already
included in the content network and having subjects, topics,
skills, skill levels, or the like identified in their metadata that
correspond to those subjects, topics, skills, skill levels, or the
like identified in the data packet received in block 1102. In some
embodiments, the metadata for the data packets already positioned
in the content network can be retrieved from the database server
104 by the server 102, and can be specifically retrieved from the
content library database server 303 by the server 102.
After similarly positioned data packets have been identified, the
process 1100 proceeds to block 1116 wherein attribute information
for those one or several similarly positioned data packets is
retrieved. In some embodiments, this attribute information can
identify, for example, difficulty levels of the similarly
positioned data packets, discrimination levels of the similarly
positioned data packets, or the like. In some embodiments, this
attribute information can be retrieved by retrieving the metadata
from the database server 104 for the similarly positioned data
packets and extracting the desired attribute information from the
retrieved metadata. In some embodiments, the similarly positioned
data packets for which the metadata is retrieved and for which the
attribute information is determined or identified can be the
similarly positioned data packets identified in block 1114.
After the attribute information for the similarly positioned data
packets has been received and/or retrieved, the process 1100
proceeds to block 1118 wherein a combined attribute value is
generated. In some embodiments, this combined attribute value
reflects the aggregate of the attribute information retrieved in
block 1116. In some embodiments, this combined value can comprise a
mean, median, mode, or any other meaningful combined value. In some
embodiments, the combined attribute value can comprise an average
difficulty level, an average discrimination level, or the like. The
combined attribute value can be, in some embodiments, generated by
the server 102 or any other desired component of the content
distribution network 100.
After the combined attribute value has been determined, the process
1100 proceeds to block 1120 wherein the value of the attribute of
the data packet received in block 1102 corresponding to the
combined attribute value is set to match the combined attribute
value. This can include, for example, setting the difficulty level
of the data packet received in block 1102 to the average difficulty
level generated in block 1118 for similarly positioned data packets
identified in block 1114 when the combined attribute value
comprises an average difficulty level. Similarly, this can include
setting the discrimination level of the data packet received in
block 1102 to the average discrimination level generated in block
1118 for the similarly positioned data packets identified in block
1114 when the combined attribute value comprises an average
discrimination level. In some embodiments, the value of the
attribute of the data packet received in block 1102 can be set to
the average attribute value generated in block 1118 by the server
102 and an indicator of this can be stored in the database server
104 and specifically in the content library database 303. In other
words, in some embodiments, the metadata of the data packet
received in block 1102 can be updated to reflect an attribute value
corresponding to the combined attribute value generated in block
1118. In such an embodiment, the metadata of the data packet
received in block 1102 can be updated to have a difficulty level
matching the average difficulty level generated in block 1118.
After the received packet attribute value has been set to the
combined attribute value, the process 1100 returns to block 1107
and proceeds as outlined above.
With reference now to FIG. 12, a flowchart illustrating one
embodiment of a process 1200 for data packet metadata stabilization
is shown. The process 1200 can be performed by all or components of
the content distribution network 100 including by, for example, one
or several servers 102. In some embodiments, these one or several
servers 102 can comprise one or several remote resources such as
can occur via cloud computing or distributed processing. The
process 1200 begins to block 1202 wherein the data packet is
received and/or identified. In some embodiments, the data packet
can be received from, for example, block 1107 of FIG. 11. In some
embodiments, the data packet can be identified from one of the data
packet stored in the database 104 specifically in the content
library database 303.
After the data packet has been received or identified, process 1200
proceeds to block 1204 wherein data packet information, also
referred to herein as data packet metadata is retrieved. In some
embodiments, data packet information can be retrieved for the data
packet received and/or identified in block 1202. In some
embodiments, the data packet metadata can identify one or several
attributes of the data packet such as, for example, the difficulty
level of all or portions of the data packet, the discrimination
level of all or portions of the data packet, or the like. In some
embodiments, the data packet metadata can be received with the data
packet in block 1202, and in some embodiments, the data packet
metadata can be retrieved from the database server 104 and
specifically from, for example, the content library database 303 of
the database server 104.
After the data packet metadata have been retrieved, the process
1200 proceeds to decision state 1206 where it is determined if one
or several attributes identified in the data packet metadata are
stable. In some embodiments, for example, the one or several
attributes can be identified in the data packet data by one or
several values, also referred to herein as attribute values. In
some embodiments, for example, one or several of these attribute
values can identify the difficulty and/or discrimination of the
data packet received and/or identified in block 1202.
In some embodiments, the data packet metadata can include time
series data tracking the one or several attribute values over time.
In some embodiments, decision state 1206 can include determining
whether the one or several attribute values are stable as indicated
by their time series data. Alternatively, in some embodiments, the
determination of the stability of the attribute values can comprise
determining the number of responses received for the data packet
received and/or identified in block 1202 and retrieving a
threshold, referred to herein as a stability threshold, from the
database server 104 and specifically from the threshold database
310 which delineates between stable attribute values and unstable
attribute values based on, for example, the number of responses
received foreign attribute value. In some embodiments, for example,
this threshold can identify a minimum number of responses to be
received before an attribute value is identified as stable. In some
embodiments, this minimum number of responses can be, for example,
100 responses, 200 responses, 500 responses, 750 responses, 1000
responses, 2000 responses, 5000 responses, 10,000 responses, 50,000
responses, 100,000 responses, and/or any other or intermediate
number of responses. In some embodiments, the determined number of
responses received for the data packet received and/or identified
in a block 1102 can be compared to the stability threshold to
determine whether the required minimum number of responses has been
received for the data packet identified in block 1202.
If it is determined that the desired number of responses has been
received, the process 1200 proceeds to block 1208 wherein a
stability indicator is added to the data packet metadata associated
with the data packet received and/or identified in block 1202
and/or wherein a stability indicator is stored within the database
server 104 in association with the data packet retrieved and/or
identified in block 1202. In some embodiments, the stability
indicator can comprise one or several values, one of which values
can be indicative of the achievement of stability for the attribute
values of the data packet received in block 1202. In some
embodiments, the adding of a stability indicator can further
comprise the generating and sending of an alert to a device such as
the supervisor device 110. In some embodiments, this alert can
identify the data packet and that the difficulty of the data packet
is stable. In some embodiments, the alert can contain other
information relevant to the data packet received and/or identified
in block 1202.
Returning again to decision state 1206, if it is determined that
the difficulty level is not stable, then the process 1200 proceeds
to block 1210 wherein one or several potential packet recipients
are identified. In some embodiments, the one or several potential
packet recipients can be, for example, one or several users
belonging to a common cohort. In some embodiments, this common
cohort can include registration in one or several groups, classes,
courses of study, disciplines, or the like. In some embodiments,
identifying the potential packet recipients can include identifying
one or several cohorts related to the data packet received and/or
identified in block 1202 based on, for example, data packet
metadata for the data packet received in block 1202. In some
embodiments, the database server 104 can then be queried for
information identifying one or several users belonging to these one
or several cohorts. In some embodiments, this query can be directed
to user profile database 301. The potential packet recipients can,
in some embodiments, be identified by the server 102.
In some embodiments, the set of potential packet recipients can
comprise users requesting a next data packet or an additional data
packet. In some embodiments, the set of potential packet recipients
can be formally identified in that some potential packet recipients
are designated as belonging to this set of potential packet
recipients and/or in that this set of potential packet recipients
exists in, for example, the database server 104. In some
embodiments, this set of potential packet recipients can merely
comprise the group of users requesting a next packet, and
specifically requesting a next packet that can include the data
packet identified in block 1202.
After the potential packet recipients have been identified, the
process 1200 proceeds to block 1212, wherein user data for the
potential packet recipients is retrieved and/or received. In some
embodiments, this user data can comprise the metadata for each of
the users in the group of potential packet. This metadata can be
received from the database server 104 and specifically from the
user profile database 301 by the server 102 upon query of the user
profile database 301 for that metadata by the server 102.
After the user data has been retrieved, the process 1200 proceeds
to block 1214 wherein a data packet attribute value is identified.
In some embodiments, the data packet attribute value can be one or
several values that can characterize a difficulty level of the data
packet received in block 1202 and/or that can characterize the
discrimination level of the data packet received in block 1202. In
some embodiments, the data packet metadata can be retrieved in the
database server 104 and specifically from the content library
database 303. In some embodiments, as the data packet metadata have
already been retrieved in block 1204, block 1214 can include
extracting one or several attribute values from that metadata.
After the data packet attribute value has been identified, the
process 1200 proceeds to block 1216 wherein a user attribute value
is identified. In some embodiments, the user attribute value can be
one or several values that characterize, for example, the skill
level of the user, a learning style of the user, or the like. In
some embodiments, the user attribute value can be identified from
user metadata that can be retrieved from the database server 104
and specifically from the user profile database 301.
After the skill level has been identified, the process 1200
proceeds to block 1218 wherein a recipient of the data packet is
selected. In some embodiments, the recipient is the user who will
receive the data packet received and/or identified in block 1202.
In some embodiments, the recipient of the data packet can be
selected based on, for example, comparison of one or several data
packet attribute values in one or several user attribute values.
The recipient of the data packet can be selected by the server
102.
After the recipient of the packet has been selected, the process
1200 proceeds to block 1220 wherein a non-impact value is
associated with the recipient. In some embodiments, the non-impact
value identifies the data packet received in block 1202 and
selected for delivery to the recipient selected in block 1218 as
excluded from calculations of user attribute values. In some
embodiments, for example, because instability of the attribute
values of the data packet as determined in decision state 1206,
modifications to the user metadata with results of a user response
relating to the data packet would be unreliable. Accordingly, the
non-impact value is associated with the recipient and the data
packet to be provided to the recipient so that the recipient's user
metadata is not modified based on the result of any response
provided by the recipient. In some embodiments, the non-impact
value can be associated with the recipient by storing the
non-impact value in the database server 104 and specifically in the
user profile database 301. In some embodiments, the non-impact
value can be further associated with the data packet received in
block 1202 by storing the non-impact value in the content library
database 303 and linked to the data packet received in block
1202.
After the nonimpact value has been associated with the recipient,
the process 1200 proceeds to block 1222 wherein the data packet is
provided to the recipient. In some embodiments, the data packet can
be provided to the recipient via the user device 106, and
specifically by providing the data packet to the presenter module
672 which can then provide the data packet to the view module 674
which can use the I/O subsystem 526 to provide the data packet
and/or portions of the data packet to the user of the user device
106.
After the data packet has been provided, the process 1200 proceeds
to block 1224 wherein the responses received. In some embodiments,
the responses received from the user by the server 102.
Specifically, the user can provide a response to the data packet
provided in block 1222 at the user device 106 via the I/O subsystem
526 which can pass the response to the view module 674. To view
module can then provide the response the presenter module 672, and
bus the response can be received by the server 102.
After the responses have been received, the process 1200 proceeds
to block 1226 wherein the responses are translated into an
observable. In some embodiments, the response translated into an
observable is the response received in block 1224, which response
is provided by the recipient subsequent to receiving the data
packet. In some embodiments, translating the received response into
an observable includes evaluating the received response to
determine if the received response includes one or several
attributes, or alternatively evaluating the received response to
determine if the received response is a desired response. In some
embodiments, the response can be translated into an observable by
the response system 406 and/or the response processor 678.
After the response has been translated into an observable, the
process 1200 proceeds to block 1228, wherein the data packet
metadata and/or data packet model is updated. In some embodiments,
the data packet metadata and/or data packet model associated with
the data packet provided in block 1222 can be updated based on the
observable generated in block 1226 via the translation of the
response received in block 1224. In some embodiments, this can
include updating one or several data packet attribute values
contained in the data packet metadata, and specifically can include
updating a difficulty level of the data packet according to whether
the response received in block 1224 was correct or incorrect. In
some embodiments, the data packet metadata and/or data packet can
be updated by the summary model system 404 and/or the model engine
682. The details of the updating of the data packet metadata and/or
data packet model will be discussed in greater detail with respect
to the process 1400 of FIG. 14 below. After the data packet
metadata and/or data packet model has been updated, the process
1200 returns to decision state 1206 and proceeds as outlined
above.
With reference to FIG. 13, a flowchart illustrating one embodiment
of a process 1300 for selecting recipient of the data packet shown.
In some embodiments, the process 1300 can be performed in the place
of as a part of block 1218 of FIG. 12. The process 1300 can be
performed by any component of the content distribution network 100
including, for example, the server 102. The process begins at block
1302 wherein a subset of users is selected. In some embodiments,
the subset of users selected is based on only portions of the data
packet metadata of the data packet received in block 1202 and/or
the user metadata. In some embodiments, the subset of users can be
selected from the potential packet recipients identified in block
1210 of FIG. 12. In some embodiments, the subset of users can be
selected by identifying one or several users within the group of
potential packet recipients having a skill level corresponding to
and/or matching the difficulty level and/or the discrimination
level of the data packet received in block 1202.
After the subset of users has been selected, the process 1300
proceeds to block 1304 wherein one of the subset of users is
selected for analysis in some embodiments, the selected one of the
subset of users can be the user that has not been previously
selected for analysis. In some embodiments, the selecting one user
for analysis can include identifying the users in the subset of
users that have not been previously selected for analysis and then
selecting one of those identified users. In some embodiments, when
a user is selected for analysis, a value indicative of selection
can be associated with that user. The one of the subset users can,
in some embodiments, be selected by the server 102.
After one of the subset of users has been selected for analysis,
process 1300 proceeds to block 1306 wherein user load levels of the
selected user are determined. In some embodiments, the user load
levels can comprise data that can be contained in the user metadata
and that can identify, for example, the amount of time, or the
number of data packets for a user to complete a task relating to
the data packet received in block 1202. In some embodiments,
determining the user load levels can comprise extracting this
information from the user metadata with, for example, the server
102. After the user load levels have been determined, the process
1300 proceeds to block 1308 wherein a load threshold is retrieved.
In some embodiments, below threshold delineates between acceptable
load levels and unacceptable load levels. The low threshold can be
stored in the database server 104, and specifically in the
threshold database 310. In some embodiments, the load threshold can
be generic. In some embodiments the load threshold can be specific
to, for example, one or several data packets, courses, tasks, or
the like. In some embodiments, the load threshold retrieved in
block 1308 can be relevant to the data packet received in block
1202.
After the load threshold has been retrieved, the process 1300
proceeds to block 1310 wherein a predicted load value is generated.
In some embodiments, the predicted load value can comprise the load
level determined in block 1306 adjusted to reflect inclusion of the
data packet received in block 1202. In some embodiments, this can
measure and/or predict the user load level if the data packet
received in block 1202 is provided to the user, or in other words,
if that user is selected as the recipient. In some embodiments, the
predicted load value can be generated by the server 102.
After the predicted load value has been generated, the process 1300
proceeds to block 1312 when the predicted load value is compared to
the load threshold. In some embodiments, this comparison can
include associating a first value with the user selected in block
1304 if the comparison of the load value and the load threshold
indicates an acceptable load level and the second value can be
associated with the user selected in block 1304 if the comparison
of the load value and the load threshold indicates an unacceptable
load level. In some embodiments, the predicted load value can be
compared to the load threshold by the server 102.
After the predicted load value has been compared to the load
threshold, the process 1300 proceeds to decision state 1314 wherein
it is determined if the load level is acceptable. In some
embodiments, this can include determining if the first value or the
second value is associated with the user selected in block 1304. If
the second value is associated with the user selected in block 1304
and the load level is thus unacceptable, then the process 1300
proceeds to block 1316 and the user selected in block 1304 is
removed from the set of potential packet recipients.
After the user has been removed from the set of potential packet
recipients, returning again to decision state 1314, if it is
determined that the load level is acceptable, then the process 1300
proceeds to decision state 1320 wherein it is determined if there
are additional users. In some embodiments, this can include
determining if there are additional, unselected users in the subset
of users selected in block 1302. If it is determined that there are
additional, unselected users, then the process 1300 returns to
decision state 1304 and proceeds as outlined above.
If it is determined there no additional users, then the process
1300 proceeds to block 1322 wherein a recipient is designated. In
some embodiments, this can include determining whether any of the
users in the set of potential users is associated with a first
value indicative of an acceptable load level. If only a single user
is associated with the first value indicative of an acceptable load
level, then that single user is designated as the recipient by the
server 102. If multiple users are associated with the first value
indicative of an acceptable vote level, then one of those multiple
users can be designated as the recipient. In some embodiments, for
example, this one of the multiple users can be randomly selected
from the multiple users that are associated with the first value
by, for example the server, and in some embodiments, this one of
the multiple users can be selected based on the closeness of the
match between the attribute values of the data packet received in
block 1202 and attribute values of the users, the degree to which
users' predicted load levels are acceptable, or the like. After the
recipient has been designated, the process 1300 proceeds to block
1324 and continues at block 1220 of FIG. 12.
With reference now to FIG. 14, a flowchart illustrating one
embodiment of a process 1400 for automatically updating data packet
metadata and/or automatically updating a data packet model is
shown. In some embodiments, the process 1400 can be performed as a
part of or in place of the step of block 1228 of FIG. 12, and in
some embodiments, the process 1400 can be performed independent of
the process of FIG. 12. The process 1400 can be performed by the
server 102, and specifically by, for example, the response system
406, the response processor 678, the summary model system 404
and/or the model engine 682. The process 1400 begins at block 1402
wherein a response is received. In some embodiments, the response
can be received from a user device 106 via the I/O subsystem 526,
the communication subsystem 532, the communications network 102,
the view module 674, and/or the presenter module 672. In some
embodiments, the response can be associated with a previously
provided data packet that can be selected for the user based on one
or several of the user metadata, the data packet metadata, or the
like.
After the responses have been received, the process 1400 proceeds
to block 1404 wherein the data packet associated with the received
responses is identified. In some embodiments, for example, the
response can include information relating to the data packet
prompting the response. In some embodiments, for example, this
information can be extracted from the response and can be used to
identify the data packet provided to the user previous to the
response and giving rise to the response. In some embodiments, the
data packet can be identified by the server 102.
After the data packet has been identified, the process 1400
proceeds to block 1406 wherein the data packet metadata is
retrieved. In some embodiments, the data packet metadata can
comprise information relating to one or several attributes of the
data packet. In some embodiments, the data packet metadata can be
associated with the data packet identified in block 1404 and the
data packet metadata can be retrieved from the database server 104
and specifically from the content library database 303.
In some embodiments, the one or several attributes can be
represented and/or determined through one or several models, such
as statistical models. In some embodiments, these models can be
continuously or periodically updated by the content distribution
network 100 when one or several new responses are received. In some
embodiments, these models can be stored in, for example, the user
profile database 301, the content library database 303, the model
database 309, or any of the other databases 104. In some
embodiments, a model can be associated with a single objective such
that the model is not applicable to other objectives, and in some
embodiments, the model can be associated with a plurality of
objectives.
In some embodiments, a user skill level and/or a data packet
difficulty level can be determined via a model such as a
statistical model based on, for example, a localized distribution
including, for example, a Gaussian distribution, a Beta
distribution or a Beta family distribution, a Lognormal
distribution, or the like. In some embodiments, the data packet
difficulty can be determined from the mode of a localized
distribution such as the mode of a Gaussian distribution and/or a
piece-wise Gaussian distribution, and a user skill level can
likewise be determined from the mode of a localized distribution
such as the mode of a Gaussian distribution and/or a piece-wise
Gaussian distribution.
One example of a piece-wise Gaussian distribution 1500 is shown in
FIG. 15. As seen in FIG. 9, the piece-wise Gaussian distribution
1500 has an x-axis labelled "x" and a y-axis labelled "density." As
depicted in FIG. 15, the units of the x-axis indicate a skill level
of the user(s) associated with the piecewise Gaussian distribution
1500, with the skill level increasing in a positive correlation
with the values of the x-axis. The units of the y-axis, density,
indicate a density that describes the probability of a random
variable selected according to the piecewise Gaussian distribution
1500 to take on a value on the x-axis.
The piecewise Gaussian distribution 1500 includes a curve 1502 that
has a first piece 1504 forming the right side of the piecewise
Gaussian distribution 1500, and a second piece 1506 forming the
left side of the piecewise Gaussian distribution 1500. In some
embodiments, the mode of the piecewise Gaussian distribution 1500
can represent the skill level of the user and/or the difficulty
level of the data packet.
In some embodiments, the property of the Gaussian distribution,
such as, for example, the width of the Gaussian distribution or of
the piecewise Gaussian distribution can represent or positively
correlate to the level of certainty, and/or can be used to
determine an error value. In some embodiments, the level of
uncertainty and/or an error value can be mathematically calculated
based on one or several variables and/or parameters tied to the
Gaussian distribution and/or the piecewise Gaussian distribution
1500.
In some embodiments, the piecewise Gaussian distribution 1500 can
further include a first level of certainty and/or first error value
associated with the first piece 1504 of the piecewise Gaussian
distribution 1500, and a second level of certainty and/or a second
error value associated with the second piece 1506 of the piecewise
Gaussian distribution 1500. In such embodiments, the width of the
Gaussian distribution and/or piecewise Gaussian distribution 1500,
and/or the width of one or both of the pieces 1504, 1506 of the
piecewise Gaussian distribution 1500 can vary in a positive
relation with the level of uncertainty and/or with the error value.
Thus, as the width decreases, the level of uncertainty and/or the
error value decreases, and as the width increases, the level of
uncertainty and/or the error value increases.
In some embodiments, the first piece 1504 and the second piece 1506
can have different properties to represent different traits of one
or several users and/or of one or several data packets.
Specifically, and as seen in FIG. 15, the tail of the first piece
1504 of the piecewise Gaussian distribution 1500 extend farther
than the tail of the second piece 1506 of the piecewise Gaussian
distribution 1500. In some embodiments, this serves to provide a
semi-ratcheting effect to changes to the piecewise Gaussian
distribution 1500 when either desired responses or undesired
responses are received. Specifically, this semi-ratcheting effect
results in a user's skill level and/or the difficulty level of a
data packet more quickly increasing when a desired response is
received than decreasing when an undesired response is received.
This can, in some embodiments, correlate to the user property of
accepting data, or more specifically, gaining knowledge faster than
losing knowledge.
This semi-ratcheting effect is illustrated in FIG. 16 which depicts
a plurality of piecewise Gaussian distributions 1520. The plurality
of piecewise Gaussian distributions 1520 includes a prior
distribution 1522, a correct distribution 1524, which is the
Gaussian distribution resulting from receipt of a desired response
to an assessment data packet, and an incorrect distribution 1526,
which is the piecewise Gaussian distribution resulting from receipt
of an undesired response to an assessment data packet.
As depicted in FIG. 16, the prior response has a mode of -0.2, and
thus depicts a user skill level and/or data packet difficulty level
of -0.2. This skill level and/or data packet difficulty level
shifts, in response to the received desired response to 2.2, and
shifts, in response to the received undesired response of -1.5.
Thus, the semi-ratcheting effect of the piecewise Gaussian
distribution is that the skill level and/or data packet difficulty
level increases a total of 2.5 as a result of the received desired
response and only decreases a total of 1.3 as a result of the
received undesired response. As further seen, each of the correct
and incorrect distributions 1524, 1526 is narrower than the prior
distribution 1522, indicating lower levels of uncertainty and
greater levels of certainty associated with those models. In some
embodiments, the degree to which the width of the piece-wise
Gaussian distribution 1500 can shrink can be limited. In some
embodiments, for example, the first piece 1504 of the piece-wise
Gaussian distribution 1500 can be prohibited from having a scale
factor smaller than, for example, approximately: 3.0; 2.5; 2.0;
1.75; 1.5; 1.25; 1.0; 0.75; 0.5; 0.25; and/or any other or
intermediate value, and in some embodiments, for example, the
second piece 1506 of the piece-wise Gaussian distribution 1500 can
be prohibited from having a scale factor smaller than, for example,
approximately: 3.0; 2.5; 2.0; 1.75; 1.5; 1.25; 1.0; 0.75; 0.5;
0.25; 0.15; 0.1; 0.05; and/or any other or intermediate value. As
used herein, "approximately" identifies a range about the therewith
associated value, which range is +/-25%, 20%, 15%, 10%, 5%, and/or
any other or intermediate percent of that therewith associated
value.
Returning again to FIG. 14, after the data packet metadata have
been retrieved, the process 1400 proceeds to block 1408 wherein the
response data is evaluated. In some embodiments, this can include
the translation of the response into an observable by, for example,
the response processor 678. In some embodiments, this translation
of the response into an observable can include determining whether
the response is a desired response or an undesired response.
Evaluation the response can be performed by the response processor
678 based on answer data associated with the data packet and, in
some embodiments, stored in the content library database 303.
After the response data has been evaluated, the process 1400
proceeds to block 1410 wherein one or several attribute values of
the data packet for which the response is received in block 1402
are updated. In some embodiments, this can include updating the
user attributes to reflect a new skill level and/or new confidence
level or error value, and in some embodiments, this can include
updating the objective and/or data packet data to reflect a new
difficulty level and/or new confidence level or error value. In
some embodiments, the date of attributes in block 1410 can include
the updating of both user attributes and data packet attributes
unless the user associated with the response received in block 1402
is associated with a non-impact value as can be generated in block
1220 of FIG. 12.
In some embodiments, the user data and/or data packet data can be
updated to reflect the desired or undesired response received. In
some embodiments in which the user provides an undesired response,
the user skill level can stay the same and/or be negatively changed
and/or the data packet difficulty level can increase and/or stay
the same, and in some embodiments in which the user provides the
desired response, the user skill level can positively change and/or
the data packet difficulty level can decrease and/or stay the same.
In some embodiments, the user skill level can be changed according
to one or several predictive models which can be, for example, one
or several probabilistic models.
In some embodiments in which one or several user attributes and/or
objective or data packet attributes are modeled according to a
localized distribution such as a Gaussian distribution and/or a
piecewise Gaussian distribution, these models can be updated using
a mathematical approach including, for example, a Bayesian
approach. In such an embodiment, the updated model 1524, 1526 can
be determined based on the combination of prior model 1522 and the
calculated probability of the user providing the desired response
to the provided data packet. Thus, in some embodiments, the prior
model can be adjusted based on the skill level and/or difficulty
level most likely correlating to the received response. In some
embodiments, this adjustment can be limited to within the error
function and/or error value so as to stabilize the change to the
prior model.
In one embodiment, for example, the probability of the user
providing the desired response can be based on the user skill level
and/or the difficulty of the data packet, and can be determined
using an Item Response Theory ("IRT") model such as, for example, a
Rasch model and/or a sigmoid and/or logistic curve. This
probability of correctly answering the question can be input into,
for example, a Gaussian model of the student skill level, which
Gaussian model can be updated according to a Bayesian technique to
estimate a new student skill level and/or a new data packet
difficulty level.
After the additional data has been updated, the process 1400
proceeds to decision state 1412, wherein it is determined if there
are any additional responses. In some embodiments, this can include
determining whether there are any other responses received for the
data packet identified in block 1404. If there are additional
responses, then the process returns to block 1402 and proceeds as
outlined above. If there are no additional responses then the
process 1400 proceeds to block 1414, wherein an alert is generated
and sent to, for example, the supervisor device 110. In some
embodiments, this alert can be generated and sent as described
above. In some embodiments, this alert can comprise information
identifying, for example, the occurrence of the update to the
attributes and/or data identifying the effect of the update.
In some embodiments, and in the place of providing the alert, the
process 1400 at step 1414 can return to decision state 1206 and
proceed as outlined above. Alternatively, in other embodiments, the
process 1400 at step 1414 can provide the data packet identified in
block 1404 to one or several users based on the updated attribute
information.
With reference now to FIGS. 17 and 18, flowcharts illustrating one
embodiment of a process 1700 for data packet selection and delivery
are shown. In some embodiments, the process 1700 can be performed
to select data packets for presentation to a user, which selection
can be biased towards data packets associated with a lower
probability of receiving a desired response. The process 1700 can
be performed by all or portions of the CDN 100.
The process 1700 begins at block 1702 wherein a user identification
information is received. In some embodiments, this user
identification information can include a username, a username and a
password, a unique user identifier, a name of the user, or the
like. The user identification information can be, in some
embodiments, received by the server 102 from the user device 106
via the communication network 120. In some embodiments, the receipt
of user identification information can include the identification
the user associated with user identification information, and the
identification of content relevant to that user. In some
embodiments, for example, the user can input the user
identification information in the user device 106 via the I/O
subsystem 526.
After the user identification data has been received, the process
1700 proceeds to block 1704 wherein content is selected. In some
embodiments, the selected content can be content for providing to
the user identified in block 1702 and can include, for example,
text, text strings, video, audio, or the like. In some embodiments,
the content can be selected according to a progression of the
identified user through the content network and specifically, the
content can be selected by identifying the location of the user in
the content network can identifying next content for providing to
the user. In some embodiments, the content can be selected by the
server 102 and specifically by, for example, the packet selection
system 402, the recommendation engine 686, and/or as part of the
packet selection process.
After the content has been selected, the process 1700 proceeds to
block 1706 wherein the content is provided. In some embodiments,
this can include the generation and sending of one or several
electrical communications including, as a payload, the selected
content. In some embodiments, the content can be provided by the
server 102 to one or several of the user devices 106 and/or
supervisor devices 110 via the communications network 120. In some
embodiments, and as a part of the providing of the content, the
user device 106 can, via the I/O subsystem 526, launch a user
interface and present the content to the user.
After the content has been provided, the process 1700 proceeds to
block 1708 wherein a progress indicator is received. The progress
indicator can come in some embodiments, identify progress through
the provided content, and can specifically indicate what all or
portions of the provided content have been consumed by the user
and/or have been presented to the user by the user device 106. In
some embodiments, the progress indicator can be received by the
server 102 from the user device 106 via Indication network 120.
After the progress indicator has been received, the process 1700
proceeds to block 1714 wherein progress status for the user is
updated. In some embodiments, this progress status can be updated
according to the progress indicator received in block 1708. In some
embodiments, block 1708 and 1714 can be repeatedly performed while
content is being provided as indicated in block 1706 and/or while
the user device 106 is presenting content to the user. In some
embodiments, for example, a progress status indicator can be
associated with the user. This progress status indicator can be
stored in the database server 104, and specifically in one of the
databases of the database server 104 such as the user profile
database 301. In some embodiments, the progress status can identify
the content in the progress through that content made by the user.
In some embodiments, and as the server 102 receives one or several
progress indicators, the progress status can be updated to reflect
user's progress through the provided content.
After the progress status is been updated, the process 1700
proceeds to block 1716 wherein a review request is received. In
some embodiments, for example, the user can request review of
provided content. In some embodiments, the review request can be
received by the server 102 from the user device 160 and a
communication network 120 subsequent to the receipt of the review
request by the user device 106, and specifically by the I/O
subsystem 526 of the user device 106. After the review request is
then received, the process 1700 proceeds to block 1718 wherein a
review threshold is retrieved. In some embodiments, the review
threshold can delineate between when progress through the provided
content adequate to allow for review and when progress through the
provided content is inadequate to allow for review. The review
threshold can be retrieved from the database server 104, and can be
specifically retrieved from the threshold database 310.
After the review threshold has been retrieved, the process 1700 can
proceed to block 1720 wherein the review threshold is compared to
the progress status. In some embodiments, this comparison can be
performed by the server 102. In some embodiments, the server 102
can associate a first value indicative of meeting and/or exceeding
the threshold with the user and/or with the review request when the
comparison of the progress status and the review threshold
indicates that review is allowable, and the server 102 can
associate a second value indicative of failing to meet and/or
failing to exceed the threshold with the user and/or with the
review request when the comparison of the progress status and the
review threshold indicates that review is unallowable.
The process proceeds to decision state 1722, wherein it is
determined whether to provide review data packets to the user. In
some embodiments, this determination can be made by the server 102
based on the result of the comparison performed in block 1720. In
some embodiments, for example, the server 102 can determine whether
the first value is associated with the user and/or with the review
request or if the second value is associated with the user and/or
with the review request. If it is determined that the review is
unallowable, then the process 1700 can return to block 1706 and
proceed as outlined above. Alternatively, if it is determined that
review is allowable, then the process 1700 can proceed to block
1724 wherein user metadata are retrieved. In some embodiments, the
user metadata can be retrieved by the server from the database
server 104 and specifically from the user profile database 301.
After the user metadata have been retrieved, the process 1700
proceeds to block 1726 wherein one or several review data packets,
also referred to herein as items, are identified. In some
embodiments, the review data packets can be some or all of the data
packets that can be stored in the database server 104 and
specifically in the content library database 303. In some
embodiments, the review data packets that are identified can be
data packets relevant to the content selected in block 1704 and
provided in block 1706. The review data packets can be identified
by the server 102 from the data packets stored within the content
library database. In some embodiments, this identification can
include the querying of the database server 104 for data packets
relating to the content selected in block 1704 and special provided
in block 1706, and the receipt of a response to that query.
After review data packets have been identified, the process 1700
proceeds to block 1728 wherein data packet metadata for those
identified review data packets are retrieved. In some embodiments,
the data packet metadata can be retrieved by the server 102 from
the database server 104, and specifically from the content library
database 303 of the database server 104. After the data packet
metadata for the review data packets identified in block 1726 have
been retrieved, the process 1700 proceeds to block 1730 and then
continues at block 1732 of FIG. 18.
At block 1732, one of the review data packets is selected. In some
embodiments, this one of the review data packets can be selected by
the server 102. In some embodiments, this one of the review data
packets can be randomly selected by the server 102 and/or can be
selected according to one or several preferences, priorities, or
the like. In some embodiments, these preferences, priorities, or
the like can be identified within one of the databases of the
database server 104.
After an item has been selected, the process 1700 proceeds to block
1734 wherein a probability of a desired response for the selected
item is calculated. In some embodiments, the probability of a
desired response for the selected item can be calculated based on,
for example, user metadata and data packet metadata. Specifically,
the probability of a desired response for the selected item can be
calculated based on a skill level indicated in the user metadata
and the difficulty indicated in the data packet metadata. In some
embodiments, this probability can be calculated according to a
statistical model and/or mathematical model. In some embodiments,
for example, this probability can be calculated based on the
psychometric theory such as, for example, Item Response Theory.
This probability can be calculated by the server 102.
After the probability has been calculated, the process 1700
proceeds to block 1736 when the probability calculated in block
1734 is stored. In some embodiments, this probability can be stored
in the system memory 318 or in the database server 104 and
specifically in one of the databases of the database server 104
such as, for example, the user profile database 301 and/or the
content library database. In some embodiments, this probability can
be linked to the user for whom it is calculated and/or to the data
packet for which it is calculated. In some embodiments, these links
can comprise one or several pointers in the database in which the
probability is stored.
After the probability has been stored, the process 1700 proceeds to
decision state 1738 when it is determined if there are additional,
unevaluated items. In some embodiments, for example, step 1732
through 1736 can be repeated for some or all of the potential data
packets. In some embodiments, decision state 1738 can include
determining whether said 1732 through 1736 should be performed for
additional potential data packets. If it is determined that there
are additional items for which step 1732 to 1736 should be
performed, then the process 1700 returns to block 1732 and proceeds
as outlined above.
If it is determined that there are no additional items for which
step 1732 through 1736 should be performed, then the process 1700
proceeds to block 1740 wherein weighting data are generated and/or
retrieved. In some embodiments, the weighting data can identify one
or several weighting values to be associated with data packets
based on the calculated probability of a desired response. In some
embodiments, the weighting data can comprise the output of a
weighting algorithm, weight function, curve, or the like. In some
embodiments, the weighting data, and specifically the weighting
curve can comprise a normal or Gaussian distribution, or portion
thereof. In one particular embodiment, the weighting curve can
comprise a portion of the Gaussian distribution and/or a truncated
Gaussian distribution.
In some embodiments in which the weighting data comprises a weight
function or a weighting curve, the weight function or the weighting
curve can be positioned and/or shaped to prefer data packets
associated with one or several desired probabilities of receiving a
desired response. In some embodiments, for example, the weight
function or the weighting curve can be positioned and/or shaped to
prefer data packets having a lower probability of receiving a
desired response such that the lower the probability of receipt of
a desired response, the higher the preference.
One embodiment of a weight function is shown in FIG. 19, which
specifically shows a truncated Gaussian distribution 1900. As seen
in FIG. 19, the truncated Gaussian distribution 1900 can comprise
approximately half of the Gaussian distribution having a vertical
axis corresponding to a selection weight and/or a weighting value,
and the horizontal axis corresponding to a probability of a desired
response. The Gaussian distribution can be applied to the
probabilities of a desired response such that the mode 1902 of the
Gaussian distribution 1900 is located at the point of zero
probability of a desired response, and the tail 1904 extends to the
point of a 1.0 probability of a desired response. Specifically, the
Gaussian distribution can be applied such that the argument of the
distribution is the probability of the desired response.
Returning again to block 1740 of FIG. 19. The weighting data can be
retrieved by the server 102 from the database server 104. In some
embodiments, the weighting data can be stored in one of the
databases of the database server 104, and specifically can be
stored in the model database 309, and specifically in the weighting
database in the model database 309.
After the weighting data has been retrieved or generated, the
process 1700 proceeds to block 1742, wherein the items are
classified. In some embodiments, this can include identifying if
any of the items have the same probability of receiving a correct
response. If one or several items having the same probability of
receiving the desired response are identified, then these one or
several items are placed in one or several groups with other items
having the same weight. This categorization can be performed by the
server 102.
After the items have been categorized, the process 1700 proceeds to
block 1744, wherein the weighting data is applied to the data
packets. In some embodiments, the application of the weighting data
to the data packets can result in each of the data packets and/or
each of the categories of data packets having a weight. In some
embodiments, the application of the weighting data can be according
to the probability of a data packet receiving the desired response
and/or the probability of a category of data packets with a common
probability of receiving the correct response, receiving the
correct response. In embodiments in which one or several data
packets sharing a common probability are grouped together, the
weighting data can be calculated according to a composition of
functions and the weighting can be applied to the group and/or to
the data packets in the group. In some embodiments, the weight of a
data packet and/or of a group of data packets can be generated
and/or the data packets can be selected according to probabilities
given by weights by the server 102.
After the weights have been applied, the process 1700 proceeds to
block 1746, wherein a set size is determined. In some embodiments,
a set of data packets is provided to the user, and the set size
determines the number of data packets provided in this set. In some
embodiments, the set size can be determined based on an input
received from the user device and/or based on information
associated with the potential data packets. In some embodiments,
data identifying the set size can be retrieved from the database
server 104, and specifically from the content library database
303.
After the set size has been determined, the process 1700 proceeds
to block 1748, wherein the set is filled. In some embodiments, the
filling of the set can include the selection of data packets from
the potential data packets for inclusion in the set. In some
embodiments, after a data packet has been selected, the data packet
can be added to the set and can be removed from the potential data
packets so as to prevent the reselection of that data packet. In
some embodiments, the removal of one or several data packets from
the potential data packets can result in a changing of the weight
of the remaining others of the potential data packets, and in some
embodiments, the removal of one or several data packets from the
potential data packets does not result in a changing of the weight
of the remaining others of the potential data packets. After the
data packet has been added to the set, the actual size of the set
can be compared to the determined set size. If the set is filled,
then the process can proceed to block 1750, if the set is not
filled, the one or several additional data packets can be added to
the set until the set is filled. The set can be filled, in some
embodiments, by the server 102, and data identifying data packets
in the set can be stored in the database server 104, and
specifically in the user profile database 301 and/or in the content
library database 303.
After the set has been filled, the process 1700 proceeds to block
1750, wherein the data packet can be delivered. In some
embodiments, the data packets can be delivered to the user device
106 from the server 102 via the communications network 120. In some
embodiments, the delivery of the data packets can include the
generation of one or several electrical signals and/or electrical
communications and sending of these one or several electrical
signals and/or electrical communications from the server 102 to the
user device 106 via communications network 120. In some
embodiments, these one or several electrical signals and/or
electrical communications can include the set of data packets
and/or one or several of the data packets as a payload.
After the data packets have been delivered, the process 1700
proceeds to block 1752 wherein one or several response
communications are received. In some embodiments, these one or
several response communications can be received from, for example,
the user device 106 by the server 102 via the communication network
120. In some embodiments, the response communications can comprise
one or several electrical signals and/or electrical communications.
After the response communications have been received, the process
1700 proceeds to block 1754 wherein the one or several received
responses are evaluated. In some embodiments, this evaluation can
be performed by the response processor 678 as part of the response
process 676 and/or by the response system 406. In some embodiments,
the responses can be evaluated according to evaluation data stored
in and/or retrieved from the database server 104 and specifically
from the content library database 303 and/or the evaluation
database 308.
After the one or several received responses have been evaluated,
the process 1700 proceeds to block 1756 wherein metadata for the
user for whom the one or several responses were received in block
1752 are updated. In some embodiments, this user metadata can be
updated to indicate an increased skill level, or a decreased skill
level, completion of one or several data packets, or the like.
After the user metadata have been updated, the process 1700
proceeds to block 1758 wherein one or several alerts are generated
and/or sent. In some embodiments, these one or several alerts can
be generated and/or sent when the user skill level identified in
the user metadata drops below a threshold value such as, for
example, when a skill level identified in the user metadata drops
below the threshold value. In some embodiments, the alert can be
generated and sent when a number of undesired responses exceeds a
threshold value. In some embodiments, this alert can include an
identifier of the event that triggered the alert, and in some
embodiments, the alert can include remedial information, an
intervention or intervention plan, or the like. The alert can be
generated by the server 102 and can be sent to the user device 106
and/or to the supervisor device 110.
A number of variations and modifications of the disclosed
embodiments can also be used. Specific details are given in the
above description to provide a thorough understanding of the
embodiments. However, it is understood that the embodiments may be
practiced without these specific details. For example, well-known
circuits, processes, algorithms, structures, and techniques may be
shown without unnecessary detail in order to avoid obscuring the
embodiments.
Implementation of the techniques, blocks, steps and means described
above may be done in various ways. For example, these techniques,
blocks, steps and means may be implemented in hardware, software,
or a combination thereof. For a hardware implementation, the
processing units may be implemented within one or more
application-specific integrated circuits (ASICs), digital signal
processors (DSPs), digital signal processing devices (DSPDs),
programmable logic devices (PLDs), field programmable gate arrays
(FPGAs), processors, controllers, micro-controllers,
microprocessors, other electronic units designed to perform the
functions described above, and/or a combination thereof.
Also, it is noted that the embodiments may be described as a
process which is depicted as a flowchart, a flow diagram, a swim
diagram, a data flow diagram, a structure diagram, or a block
diagram. Although a depiction may describe the operations as a
sequential process, many of the operations can be performed in
parallel or concurrently. In addition, the order of the operations
may be re-arranged. A process is terminated when its operations are
completed, but could have additional steps not included in the
figure. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process
corresponds to a function, its termination corresponds to a return
of the function to the calling function or the main function.
Furthermore, embodiments may be implemented by hardware, software,
scripting languages, firmware, middleware, microcode, hardware
description languages, and/or any combination thereof. When
implemented in software, firmware, middleware, scripting language,
and/or microcode, the program code or code segments to perform the
necessary tasks may be stored in a machine readable medium such as
a storage medium. A code segment or machine-executable instruction
may represent a procedure, a function, a subprogram, a program, a
routine, a subroutine, a module, a software package, a script, a
class, or any combination of instructions, data structures, and/or
program statements. A code segment may be coupled to another code
segment or a hardware circuit by passing and/or receiving
information, data, arguments, parameters, and/or memory contents.
Information, arguments, parameters, data, etc. may be passed,
forwarded, or transmitted via any suitable means including memory
sharing, message passing, token passing, network transmission,
etc.
For a firmware and/or software implementation, the methodologies
may be implemented with modules (e.g., procedures, functions, and
so on) that perform the functions described herein. Any
machine-readable medium tangibly embodying instructions may be used
in implementing the methodologies described herein. For example,
software codes may be stored in a memory. Memory may be implemented
within the processor or external to the processor. As used herein,
the term "memory" refers to any type of long term, short term,
volatile, nonvolatile, or other storage medium and is not to be
limited to any particular type of memory or number of memories, or
type of media upon which memory is stored.
Moreover, as disclosed herein, the term "storage medium" may
represent one or more memories for storing data, including
read-only memory (ROM), random access memory (RAM), magnetic RAM,
core memory, magnetic disk storage mediums, optical storage
mediums, flash memory devices and/or other machine readable mediums
for storing information. The term "machine-readable medium"
includes, but is not limited to portable or fixed storage devices,
optical storage devices, and/or various other storage mediums
capable of storing that contain or carry instruction(s) and/or
data.
While the principles of the disclosure have been described above in
connection with specific apparatuses and methods, it is to be
clearly understood that this description is made only by way of
example and not as limitation on the scope of the disclosure.
* * * * *